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Brand YE, Kluge F, Palmerini L, Paraschiv-Ionescu A, Becker C, Cereatti A, Maetzler W, Sharrack B, Vereijken B, Yarnall AJ, Rochester L, Del Din S, Muller A, Buchman AS, Hausdorff JM, Perlman O. Automated Gait Detection in Older Adults during Daily-Living using Self-Supervised Learning of Wrist-Worn Accelerometer Data: Development and Validation of ElderNet. Res Sq 2024:rs.3.rs-4102403. [PMID: 38559043 PMCID: PMC10980143 DOI: 10.21203/rs.3.rs-4102403/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Progressive gait impairment is common in aging adults. Remote phenotyping of gait during daily living has the potential to quantify gait alterations and evaluate the effects of interventions that may prevent disability in the aging population. Here, we developed ElderNet, a self-supervised learning model for gait detection from wrist-worn accelerometer data. Validation involved two diverse cohorts, including over 1,000 participants without gait labels, as well as 83 participants with labeled data: older adults with Parkinson's disease, proximal femoral fracture, chronic obstructive pulmonary disease, congestive heart failure, and healthy adults. ElderNet presented high accuracy (96.43 ± 2.27), specificity (98.87 ± 2.15), recall (82.32 ± 11.37), precision (86.69 ± 17.61), and F1 score (82.92 ± 13.39). The suggested method yielded superior performance compared to two state-of-the-art gait detection algorithms, with improved accuracy and F1 score (p < 0.05). In an initial evaluation of construct validity, ElderNet identified differences in estimated daily walking durations across cohorts with different clinical characteristics, such as mobility disability (p < 0.001) and parkinsonism (p < 0.001). The proposed self-supervised gait detection method has the potential to serve as a valuable tool for remote phenotyping of gait function during daily living in aging adults.
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Küderle A, Ullrich M, Roth N, Ollenschläger M, Ibrahim AA, Moradi H, Richer R, Seifer AK, Zürl M, Sîmpetru RC, Herzer L, Prossel D, Kluge F, Eskofier BM. Gaitmap-An Open Ecosystem for IMU-Based Human Gait Analysis and Algorithm Benchmarking. IEEE Open J Eng Med Biol 2024; 5:163-172. [PMID: 38487091 PMCID: PMC10939318 DOI: 10.1109/ojemb.2024.3356791] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Revised: 11/15/2023] [Accepted: 01/17/2024] [Indexed: 03/17/2024] Open
Abstract
Goal: Gait analysis using inertial measurement units (IMUs) has emerged as a promising method for monitoring movement disorders. However, the lack of public data and easy-to-use open-source algorithms hinders method comparison and clinical application development. To address these challenges, this publication introduces the gaitmap ecosystem, a comprehensive set of open source Python packages for gait analysis using foot-worn IMUs. Methods: This initial release includes over 20 state-of-the-art algorithms, enables easy access to seven datasets, and provides eight benchmark challenges with reference implementations. Together with its extensive documentation and tooling, it enables rapid development and validation of new algorithm and provides a foundation for novel clinical applications. Conclusion: The published software projects represent a pioneering effort to establish an open-source ecosystem for IMU-based gait analysis. We believe that this work can democratize the access to high-quality algorithm and serve as a driver for open and reproducible research in the field of human gait analysis and beyond.
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Affiliation(s)
- Arne Küderle
- Machine Learning and Data Analytics LabFriedrich-Alexander Universität Erlangen-Nürnberg (FAU)91054ErlangenGermany
| | - Martin Ullrich
- Machine Learning and Data Analytics LabFriedrich-Alexander Universität Erlangen-Nürnberg (FAU)91054ErlangenGermany
| | - Nils Roth
- Machine Learning and Data Analytics LabFriedrich-Alexander Universität Erlangen-Nürnberg (FAU)91054ErlangenGermany
| | - Malte Ollenschläger
- Machine Learning and Data Analytics LabFriedrich-Alexander Universität Erlangen-Nürnberg (FAU)91054ErlangenGermany
| | - Alzhraa A. Ibrahim
- Machine Learning and Data Analytics LabFriedrich-Alexander Universität Erlangen-Nürnberg (FAU)91054ErlangenGermany
- Department of Molecular NeurologyFAU Erlangen91054ErlangenGermany
- Computer Science Department, Faculty of Computers and InformationAssiut UniversityAssiut Governorate71515Egypt
| | - Hamid Moradi
- Machine Learning and Data Analytics LabFriedrich-Alexander Universität Erlangen-Nürnberg (FAU)91054ErlangenGermany
| | - Robert Richer
- Machine Learning and Data Analytics LabFriedrich-Alexander Universität Erlangen-Nürnberg (FAU)91054ErlangenGermany
| | - Ann-Kristin Seifer
- Machine Learning and Data Analytics LabFriedrich-Alexander Universität Erlangen-Nürnberg (FAU)91054ErlangenGermany
| | - Matthias Zürl
- Machine Learning and Data Analytics LabFriedrich-Alexander Universität Erlangen-Nürnberg (FAU)91054ErlangenGermany
| | - Raul C. Sîmpetru
- Machine Learning and Data Analytics LabFriedrich-Alexander Universität Erlangen-Nürnberg (FAU)91054ErlangenGermany
| | - Liv Herzer
- Machine Learning and Data Analytics LabFriedrich-Alexander Universität Erlangen-Nürnberg (FAU)91054ErlangenGermany
| | - Dominik Prossel
- Machine Learning and Data Analytics LabFriedrich-Alexander Universität Erlangen-Nürnberg (FAU)91054ErlangenGermany
| | - Felix Kluge
- Machine Learning and Data Analytics LabFriedrich-Alexander Universität Erlangen-Nürnberg (FAU)91054ErlangenGermany
| | - Bjoern M. Eskofier
- Machine Learning and Data Analytics LabFriedrich-Alexander Universität Erlangen-Nürnberg (FAU)91054ErlangenGermany
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3
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Kirk C, Küderle A, Micó-Amigo ME, Bonci T, Paraschiv-Ionescu A, Ullrich M, Soltani A, Gazit E, Salis F, Alcock L, Aminian K, Becker C, Bertuletti S, Brown P, Buckley E, Cantu A, Carsin AE, Caruso M, Caulfield B, Cereatti A, Chiari L, D'Ascanio I, Garcia-Aymerich J, Hansen C, Hausdorff JM, Hiden H, Hume E, Keogh A, Kluge F, Koch S, Maetzler W, Megaritis D, Mueller A, Niessen M, Palmerini L, Schwickert L, Scott K, Sharrack B, Sillén H, Singleton D, Vereijken B, Vogiatzis I, Yarnall AJ, Rochester L, Mazzà C, Eskofier BM, Del Din S. Mobilise-D insights to estimate real-world walking speed in multiple conditions with a wearable device. Sci Rep 2024; 14:1754. [PMID: 38243008 PMCID: PMC10799009 DOI: 10.1038/s41598-024-51766-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Accepted: 01/09/2024] [Indexed: 01/21/2024] Open
Abstract
This study aimed to validate a wearable device's walking speed estimation pipeline, considering complexity, speed, and walking bout duration. The goal was to provide recommendations on the use of wearable devices for real-world mobility analysis. Participants with Parkinson's Disease, Multiple Sclerosis, Proximal Femoral Fracture, Chronic Obstructive Pulmonary Disease, Congestive Heart Failure, and healthy older adults (n = 97) were monitored in the laboratory and the real-world (2.5 h), using a lower back wearable device. Two walking speed estimation pipelines were validated across 4408/1298 (2.5 h/laboratory) detected walking bouts, compared to 4620/1365 bouts detected by a multi-sensor reference system. In the laboratory, the mean absolute error (MAE) and mean relative error (MRE) for walking speed estimation ranged from 0.06 to 0.12 m/s and - 2.1 to 14.4%, with ICCs (Intraclass correlation coefficients) between good (0.79) and excellent (0.91). Real-world MAE ranged from 0.09 to 0.13, MARE from 1.3 to 22.7%, with ICCs indicating moderate (0.57) to good (0.88) agreement. Lower errors were observed for cohorts without major gait impairments, less complex tasks, and longer walking bouts. The analytical pipelines demonstrated moderate to good accuracy in estimating walking speed. Accuracy depended on confounding factors, emphasizing the need for robust technical validation before clinical application.Trial registration: ISRCTN - 12246987.
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Affiliation(s)
- Cameron Kirk
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, The Catalyst 3 Science Square, Room 3.27, Newcastle Upon Tyne, NE4 5TG, UK
| | - Arne Küderle
- Machine Learning and Data Analytics Lab, Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - M Encarna Micó-Amigo
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, The Catalyst 3 Science Square, Room 3.27, Newcastle Upon Tyne, NE4 5TG, UK
| | - Tecla Bonci
- Department of Mechanical Engineering and Insigneo Institute for in Silico Medicine, The University of Sheffield, Sheffield, UK
| | - Anisoara Paraschiv-Ionescu
- Laboratory of Movement Analysis and Measurement, Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland
| | - Martin Ullrich
- Machine Learning and Data Analytics Lab, Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Abolfazl Soltani
- Laboratory of Movement Analysis and Measurement, Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland
| | - Eran Gazit
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Francesca Salis
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
| | - Lisa Alcock
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, The Catalyst 3 Science Square, Room 3.27, Newcastle Upon Tyne, NE4 5TG, UK
- National Institute for Health and Care Research (NIHR) Newcastle Biomedical Research Centre (BRC), Newcastle University and the Newcastle Upon Tyne Hospitals NHS Foundation Trust, Newcastle Upon Tyne, UK
| | - Kamiar Aminian
- Laboratory of Movement Analysis and Measurement, Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland
| | - Clemens Becker
- Robert Bosch Gesellschaft für Medizinische Forschung, Stuttgart, Germany
| | - Stefano Bertuletti
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
| | - Philip Brown
- The Newcastle Upon Tyne Hospitals NHS Foundation Trust, Newcastle Upon Tyne, UK
| | - Ellen Buckley
- Department of Mechanical Engineering and Insigneo Institute for in Silico Medicine, The University of Sheffield, Sheffield, UK
| | - Alma Cantu
- School of Computing, Newcastle University, Newcastle Upon Tyne, UK
| | - Anne-Elie Carsin
- Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Catalonia, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Marco Caruso
- Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy
| | - Brian Caulfield
- Insight Centre for Data Analytics, University College Dublin, Dublin, Ireland
- School of Public Health, Physiotherapy and Sports Science, University College Dublin, Dublin, Ireland
| | - Andrea Cereatti
- Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy
| | - Lorenzo Chiari
- Department of Electrical, Electronic and Information Engineering «Guglielmo Marconi», University of Bologna, Bologna, Italy
- Health Sciences and Technologies-Interdepartmental Center for Industrial Research (CIRI-SDV), University of Bologna, Bologna, Italy
| | - Ilaria D'Ascanio
- Department of Electrical, Electronic and Information Engineering «Guglielmo Marconi», University of Bologna, Bologna, Italy
| | - Judith Garcia-Aymerich
- Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Catalonia, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Clint Hansen
- Department of Neurology, University Medical Center Schleswig-Holstein Campus Kiel, Kiel, Germany
| | - Jeffrey M Hausdorff
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
- Department of Physical Therapy, Sagol School of Neuroscience, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
- Rush Alzheimer's Disease Center and Department of Orthopaedic Surgery, Rush University Medical Center, Chicago, IL, USA
| | - Hugo Hiden
- The Newcastle Upon Tyne Hospitals NHS Foundation Trust, Newcastle Upon Tyne, UK
| | - Emily Hume
- Department of Sport, Exercise and Rehabilitation, Northumbria University Newcastle, Newcastle Upon Tyne, UK
| | - Alison Keogh
- Insight Centre for Data Analytics, University College Dublin, Dublin, Ireland
- School of Public Health, Physiotherapy and Sports Science, University College Dublin, Dublin, Ireland
| | - Felix Kluge
- Machine Learning and Data Analytics Lab, Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
- Novartis Institutes of Biomedical Research, Novartis Pharma AG, Basel, Switzerland
| | - Sarah Koch
- Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Catalonia, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Walter Maetzler
- Department of Neurology, University Medical Center Schleswig-Holstein Campus Kiel, Kiel, Germany
| | - Dimitrios Megaritis
- Department of Sport, Exercise and Rehabilitation, Northumbria University Newcastle, Newcastle Upon Tyne, UK
| | - Arne Mueller
- Novartis Institutes of Biomedical Research, Novartis Pharma AG, Basel, Switzerland
| | | | - Luca Palmerini
- Department of Electrical, Electronic and Information Engineering «Guglielmo Marconi», University of Bologna, Bologna, Italy
- Health Sciences and Technologies-Interdepartmental Center for Industrial Research (CIRI-SDV), University of Bologna, Bologna, Italy
| | - Lars Schwickert
- Robert Bosch Gesellschaft für Medizinische Forschung, Stuttgart, Germany
| | - Kirsty Scott
- Department of Mechanical Engineering and Insigneo Institute for in Silico Medicine, The University of Sheffield, Sheffield, UK
| | - Basil Sharrack
- Department of Neuroscience and Sheffield NIHR Translational Neuroscience BRC, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK
| | | | - David Singleton
- Insight Centre for Data Analytics, University College Dublin, Dublin, Ireland
- School of Public Health, Physiotherapy and Sports Science, University College Dublin, Dublin, Ireland
| | - Beatrix Vereijken
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, Trondheim, Norway
| | - Ioannis Vogiatzis
- Department of Sport, Exercise and Rehabilitation, Northumbria University Newcastle, Newcastle Upon Tyne, UK
| | - Alison J Yarnall
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, The Catalyst 3 Science Square, Room 3.27, Newcastle Upon Tyne, NE4 5TG, UK
- National Institute for Health and Care Research (NIHR) Newcastle Biomedical Research Centre (BRC), Newcastle University and the Newcastle Upon Tyne Hospitals NHS Foundation Trust, Newcastle Upon Tyne, UK
- The Newcastle Upon Tyne Hospitals NHS Foundation Trust, Newcastle Upon Tyne, UK
| | - Lynn Rochester
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, The Catalyst 3 Science Square, Room 3.27, Newcastle Upon Tyne, NE4 5TG, UK
- National Institute for Health and Care Research (NIHR) Newcastle Biomedical Research Centre (BRC), Newcastle University and the Newcastle Upon Tyne Hospitals NHS Foundation Trust, Newcastle Upon Tyne, UK
- The Newcastle Upon Tyne Hospitals NHS Foundation Trust, Newcastle Upon Tyne, UK
| | - Claudia Mazzà
- Department of Mechanical Engineering and Insigneo Institute for in Silico Medicine, The University of Sheffield, Sheffield, UK
| | - Bjoern M Eskofier
- Machine Learning and Data Analytics Lab, Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Silvia Del Din
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, The Catalyst 3 Science Square, Room 3.27, Newcastle Upon Tyne, NE4 5TG, UK.
- National Institute for Health and Care Research (NIHR) Newcastle Biomedical Research Centre (BRC), Newcastle University and the Newcastle Upon Tyne Hospitals NHS Foundation Trust, Newcastle Upon Tyne, UK.
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Romijnders R, Salis F, Hansen C, Küderle A, Paraschiv-Ionescu A, Cereatti A, Alcock L, Aminian K, Becker C, Bertuletti S, Bonci T, Brown P, Buckley E, Cantu A, Carsin AE, Caruso M, Caulfield B, Chiari L, D'Ascanio I, Del Din S, Eskofier B, Fernstad SJ, Fröhlich MS, Garcia Aymerich J, Gazit E, Hausdorff JM, Hiden H, Hume E, Keogh A, Kirk C, Kluge F, Koch S, Mazzà C, Megaritis D, Micó-Amigo E, Müller A, Palmerini L, Rochester L, Schwickert L, Scott K, Sharrack B, Singleton D, Soltani A, Ullrich M, Vereijken B, Vogiatzis I, Yarnall A, Schmidt G, Maetzler W. Ecological validity of a deep learning algorithm to detect gait events from real-life walking bouts in mobility-limiting diseases. Front Neurol 2023; 14:1247532. [PMID: 37909030 PMCID: PMC10615212 DOI: 10.3389/fneur.2023.1247532] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Accepted: 09/18/2023] [Indexed: 11/02/2023] Open
Abstract
Introduction The clinical assessment of mobility, and walking specifically, is still mainly based on functional tests that lack ecological validity. Thanks to inertial measurement units (IMUs), gait analysis is shifting to unsupervised monitoring in naturalistic and unconstrained settings. However, the extraction of clinically relevant gait parameters from IMU data often depends on heuristics-based algorithms that rely on empirically determined thresholds. These were mainly validated on small cohorts in supervised settings. Methods Here, a deep learning (DL) algorithm was developed and validated for gait event detection in a heterogeneous population of different mobility-limiting disease cohorts and a cohort of healthy adults. Participants wore pressure insoles and IMUs on both feet for 2.5 h in their habitual environment. The raw accelerometer and gyroscope data from both feet were used as input to a deep convolutional neural network, while reference timings for gait events were based on the combined IMU and pressure insoles data. Results and discussion The results showed a high-detection performance for initial contacts (ICs) (recall: 98%, precision: 96%) and final contacts (FCs) (recall: 99%, precision: 94%) and a maximum median time error of -0.02 s for ICs and 0.03 s for FCs. Subsequently derived temporal gait parameters were in good agreement with a pressure insoles-based reference with a maximum mean difference of 0.07, -0.07, and <0.01 s for stance, swing, and stride time, respectively. Thus, the DL algorithm is considered successful in detecting gait events in ecologically valid environments across different mobility-limiting diseases.
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Affiliation(s)
- Robbin Romijnders
- Digital Signal Processing and System Theory, Electrical and Information Engineering, Faculty of Engineering, Kiel University, Kiel, Germany
- Arbeitsgruppe Neurogeriatrie, Department of Neurology, Universitätsklinikum Schleswig-Holstein, Kiel, Germany
| | - Francesca Salis
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
| | - Clint Hansen
- Arbeitsgruppe Neurogeriatrie, Department of Neurology, Universitätsklinikum Schleswig-Holstein, Kiel, Germany
| | - Arne Küderle
- Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Anisoara Paraschiv-Ionescu
- Laboratory of Movement Analysis and Measurement, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Andrea Cereatti
- Department of Electronics and Telecommunications, Polytechnic of Turin, Turin, Italy
| | - Lisa Alcock
- Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Kamiar Aminian
- Laboratory of Movement Analysis and Measurement, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Clemens Becker
- Gesellschaft für Medizinische Forschung, Robert-Bosch Foundation GmbH, Stuttgart, Germany
| | - Stefano Bertuletti
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
| | - Tecla Bonci
- INSIGNEO Institute for In Silico Medicine, The University of Sheffield, Sheffield, United Kingdom
- Department of Mechanical Engineering, The University of Sheffield, Sheffield, United Kingdom
| | - Philip Brown
- Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, United Kingdom
| | - Ellen Buckley
- INSIGNEO Institute for In Silico Medicine, The University of Sheffield, Sheffield, United Kingdom
- Department of Mechanical Engineering, The University of Sheffield, Sheffield, United Kingdom
| | - Alma Cantu
- School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Anne-Elie Carsin
- Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain
- Faculty of Health and Life Sciences, Universitat Pompeu Fabra, Barcelona, Spain
- CIBER Epidemiología y Salud Pública, Madrid, Spain
| | - Marco Caruso
- Department of Electronics and Telecommunications, Polytechnic of Turin, Turin, Italy
| | - Brian Caulfield
- Insight Centre for Data Analytics, University College Dublin, Dublin, Ireland
- School of Public Health, Physiotherapy and Sports Science, University College Dublin, Dublin, Ireland
| | - Lorenzo Chiari
- Department of Electrical, Electronic and Information Engineering “Guglielmo Marconi”, University of Bologna, Bologna, Italy
- Health Sciences and Technologies—Interdepartmental Center for Industrial Research (CIRISDV), University of Bologna, Bologna, Italy
| | - Ilaria D'Ascanio
- Department of Electrical, Electronic and Information Engineering “Guglielmo Marconi”, University of Bologna, Bologna, Italy
| | - Silvia Del Din
- Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Björn Eskofier
- Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | | | | | - Judith Garcia Aymerich
- Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain
- Faculty of Health and Life Sciences, Universitat Pompeu Fabra, Barcelona, Spain
- CIBER Epidemiología y Salud Pública, Madrid, Spain
| | - Eran Gazit
- Center for the Study of Movement, Cognition and Mobility, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Jeffrey M. Hausdorff
- Center for the Study of Movement, Cognition and Mobility, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
- Department of Physical Therapy, Sackler Faculty of Medicine & Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Hugo Hiden
- School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Emily Hume
- Department of Sport, Exercise and Rehabilitation, Northumbria University, Newcastle upon Tyne, United Kingdom
| | - Alison Keogh
- Insight Centre for Data Analytics, University College Dublin, Dublin, Ireland
- School of Public Health, Physiotherapy and Sports Science, University College Dublin, Dublin, Ireland
| | - Cameron Kirk
- Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Felix Kluge
- Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
- Novartis Institute of Biomedical Research, Novartis Pharma AG, Basel, Switzerland
| | - Sarah Koch
- Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain
- Faculty of Health and Life Sciences, Universitat Pompeu Fabra, Barcelona, Spain
- CIBER Epidemiología y Salud Pública, Madrid, Spain
| | - Claudia Mazzà
- INSIGNEO Institute for In Silico Medicine, The University of Sheffield, Sheffield, United Kingdom
- Department of Mechanical Engineering, The University of Sheffield, Sheffield, United Kingdom
| | - Dimitrios Megaritis
- Department of Sport, Exercise and Rehabilitation, Northumbria University, Newcastle upon Tyne, United Kingdom
| | - Encarna Micó-Amigo
- Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Arne Müller
- Novartis Institute of Biomedical Research, Novartis Pharma AG, Basel, Switzerland
| | - Luca Palmerini
- Department of Electrical, Electronic and Information Engineering “Guglielmo Marconi”, University of Bologna, Bologna, Italy
- Health Sciences and Technologies—Interdepartmental Center for Industrial Research (CIRISDV), University of Bologna, Bologna, Italy
| | - Lynn Rochester
- Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
- Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, United Kingdom
| | - Lars Schwickert
- Gesellschaft für Medizinische Forschung, Robert-Bosch Foundation GmbH, Stuttgart, Germany
| | - Kirsty Scott
- INSIGNEO Institute for In Silico Medicine, The University of Sheffield, Sheffield, United Kingdom
- Department of Mechanical Engineering, The University of Sheffield, Sheffield, United Kingdom
| | - Basil Sharrack
- Department of Neuroscience and Sheffield NIHR Translational Neuroscience BRC, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, United Kingdom
| | - David Singleton
- Insight Centre for Data Analytics, University College Dublin, Dublin, Ireland
- School of Public Health, Physiotherapy and Sports Science, University College Dublin, Dublin, Ireland
| | - Abolfazl Soltani
- Laboratory of Movement Analysis and Measurement, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Digital Health Department, CSEM SA, Neuchâtel, Switzerland
| | - Martin Ullrich
- Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Beatrix Vereijken
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, Trondheim, Norway
| | - Ioannis Vogiatzis
- Department of Sport, Exercise and Rehabilitation, Northumbria University, Newcastle upon Tyne, United Kingdom
| | - Alison Yarnall
- Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
- Department of Mechanical Engineering, The University of Sheffield, Sheffield, United Kingdom
| | - Gerhard Schmidt
- Digital Signal Processing and System Theory, Electrical and Information Engineering, Faculty of Engineering, Kiel University, Kiel, Germany
| | - Walter Maetzler
- Arbeitsgruppe Neurogeriatrie, Department of Neurology, Universitätsklinikum Schleswig-Holstein, Kiel, Germany
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5
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Ibrahim AA, Adler W, Gaßner H, Rothhammer V, Kluge F, Eskofier BM. Association between cognition and gait in multiple sclerosis: A smartphone-based longitudinal analysis. Int J Med Inform 2023; 177:105145. [PMID: 37473657 DOI: 10.1016/j.ijmedinf.2023.105145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 07/02/2023] [Accepted: 07/07/2023] [Indexed: 07/22/2023]
Abstract
BACKGROUND Gait and cognition impairments are common problems among People with Multiple Sclerosis (PwMS). Previous studies have investigated cross-sectional associations between gait and cognition. However, there is a lack of evidence regarding the longitudinal association between these factors in PwMS. Therefore, the objective of this study was to explore this longitudinal relationship using smartphone-based data from the Floodlight study. METHODS Using the publicly available Floodlight dataset, which contains smartphone-based longitudinal data, we used a linear mixed model to investigate the longitudinal relationship between cognition, measured by the Symbol Digit Modalities Test (SDMT), and gait, measured by the 2 Minute Walking test (2 MW) step count and Five-U-Turn Test (FUTT) turning speed. Four mixed models were fitted to explore the association between: 1) SDMT and mean step count; 2) SDMT and variability of step count; 3) SDMT and mean FUTT turning speed; and 4) SDMT and variability of FUTT turningt speed. RESULTS After controlling for age, sex, weight, and height, there were significant correlations between SDMT and the variability of 2 MW step count, the mean of FUTT turning speed. No significant correlation was observed between SDMT and the 2 MW mean step count. SIGNIFICANCE Our findings support the evidence that gait and cognition are associated in PwMS. This may support clinicians to adjust treatment and intervention programs that address both gait and cognitive impairments.
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Affiliation(s)
- Alzhraa A Ibrahim
- Machine Learning and Data Analytics Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Bavaria, Germany; Computer Science Department, Faculty of Computers and Information, Assiut University, Egypt.
| | - Werner Adler
- Department of Medical Informatics, Biometry and Epidemiology, Friedrich-Alexander-University Erlangen-Nürnberg (FAU), Erlangen, Bavaria, Germany
| | - Heiko Gaßner
- Department of Molecular Neurology, University Hospital Erlangen, Erlangen, Bavaria, Germany; Fraunhofer Institut for Integrated Circuits, Erlangen, Bavaria, Germany
| | - Veit Rothhammer
- Department of Neurology, University Hospital Erlangen, Erlangen, Bavaria, Germany
| | - Felix Kluge
- Machine Learning and Data Analytics Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Bavaria, Germany
| | - Bjoern M Eskofier
- Machine Learning and Data Analytics Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Bavaria, Germany
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6
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Ollenschläger M, Höfner P, Ullrich M, Kluge F, Greinwalder T, Loris E, Regensburger M, Eskofier BM, Winkler J, Gaßner H. Automated assessment of foot elevation in adults with hereditary spastic paraplegia using inertial measurements and machine learning. Orphanet J Rare Dis 2023; 18:249. [PMID: 37644478 PMCID: PMC10466820 DOI: 10.1186/s13023-023-02854-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Accepted: 08/08/2023] [Indexed: 08/31/2023] Open
Abstract
BACKGROUND Hereditary spastic paraplegias (HSPs) cause characteristic gait impairment leading to an increased risk of stumbling or even falling. Biomechanically, gait deficits are characterized by reduced ranges of motion in lower body joints, limiting foot clearance and ankle range of motion. To date, there is no standardized approach to continuously and objectively track the degree of dysfunction in foot elevation since established clinical rating scales require an experienced investigator and are considered to be rather subjective. Therefore, digital disease-specific biomarkers for foot elevation are needed. METHODS This study investigated the performance of machine learning classifiers for the automated detection and classification of reduced foot dorsiflexion and clearance using wearable sensors. Wearable inertial sensors were used to record gait patterns of 50 patients during standardized 4 [Formula: see text] 10 m walking tests at the hospital. Three movement disorder specialists independently annotated symptom severity. The majority vote of these annotations and the wearable sensor data were used to train and evaluate machine learning classifiers in a nested cross-validation scheme. RESULTS The results showed that automated detection of reduced range of motion and foot clearance was possible with an accuracy of 87%. This accuracy is in the range of individual annotators, reaching an average accuracy of 88% compared to the ground truth majority vote. For classifying symptom severity, the algorithm reached an accuracy of 74%. CONCLUSION Here, we show that the present wearable gait analysis system is able to objectively assess foot elevation patterns in HSP. Future studies will aim to improve the granularity for continuous tracking of disease severity and monitoring therapy response of HSP patients in a real-world environment.
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Affiliation(s)
- Malte Ollenschläger
- Department of Molecular Neurology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Schwabachanlage 6, Erlangen, 91054, Germany.
- Machine Learning and Data Analytics Lab, Department of Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
| | - Patrick Höfner
- Machine Learning and Data Analytics Lab, Department of Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Martin Ullrich
- Machine Learning and Data Analytics Lab, Department of Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Felix Kluge
- Machine Learning and Data Analytics Lab, Department of Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Teresa Greinwalder
- Department of Molecular Neurology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Schwabachanlage 6, Erlangen, 91054, Germany
| | - Evelyn Loris
- Department of Molecular Neurology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Schwabachanlage 6, Erlangen, 91054, Germany
| | - Martin Regensburger
- Department of Molecular Neurology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Schwabachanlage 6, Erlangen, 91054, Germany
- Center for Rare Diseases Erlangen (ZSEER), Universitätsklinikum Erlangen, Erlangen, Germany
| | - Bjoern M Eskofier
- Machine Learning and Data Analytics Lab, Department of Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Jürgen Winkler
- Department of Molecular Neurology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Schwabachanlage 6, Erlangen, 91054, Germany
- Center for Rare Diseases Erlangen (ZSEER), Universitätsklinikum Erlangen, Erlangen, Germany
| | - Heiko Gaßner
- Department of Molecular Neurology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Schwabachanlage 6, Erlangen, 91054, Germany
- Fraunhofer IIS, Fraunhofer Institute for Integrated Circuits IIS, Erlangen, Germany
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7
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Micó-Amigo ME, Bonci T, Paraschiv-Ionescu A, Ullrich M, Kirk C, Soltani A, Küderle A, Gazit E, Salis F, Alcock L, Aminian K, Becker C, Bertuletti S, Brown P, Buckley E, Cantu A, Carsin AE, Caruso M, Caulfield B, Cereatti A, Chiari L, D'Ascanio I, Eskofier B, Fernstad S, Froehlich M, Garcia-Aymerich J, Hansen C, Hausdorff JM, Hiden H, Hume E, Keogh A, Kluge F, Koch S, Maetzler W, Megaritis D, Mueller A, Niessen M, Palmerini L, Schwickert L, Scott K, Sharrack B, Sillén H, Singleton D, Vereijken B, Vogiatzis I, Yarnall AJ, Rochester L, Mazzà C, Del Din S. Assessing real-world gait with digital technology? Validation, insights and recommendations from the Mobilise-D consortium. J Neuroeng Rehabil 2023; 20:78. [PMID: 37316858 PMCID: PMC10265910 DOI: 10.1186/s12984-023-01198-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Accepted: 05/26/2023] [Indexed: 06/16/2023] Open
Abstract
BACKGROUND Although digital mobility outcomes (DMOs) can be readily calculated from real-world data collected with wearable devices and ad-hoc algorithms, technical validation is still required. The aim of this paper is to comparatively assess and validate DMOs estimated using real-world gait data from six different cohorts, focusing on gait sequence detection, foot initial contact detection (ICD), cadence (CAD) and stride length (SL) estimates. METHODS Twenty healthy older adults, 20 people with Parkinson's disease, 20 with multiple sclerosis, 19 with proximal femoral fracture, 17 with chronic obstructive pulmonary disease and 12 with congestive heart failure were monitored for 2.5 h in the real-world, using a single wearable device worn on the lower back. A reference system combining inertial modules with distance sensors and pressure insoles was used for comparison of DMOs from the single wearable device. We assessed and validated three algorithms for gait sequence detection, four for ICD, three for CAD and four for SL by concurrently comparing their performances (e.g., accuracy, specificity, sensitivity, absolute and relative errors). Additionally, the effects of walking bout (WB) speed and duration on algorithm performance were investigated. RESULTS We identified two cohort-specific top performing algorithms for gait sequence detection and CAD, and a single best for ICD and SL. Best gait sequence detection algorithms showed good performances (sensitivity > 0.73, positive predictive values > 0.75, specificity > 0.95, accuracy > 0.94). ICD and CAD algorithms presented excellent results, with sensitivity > 0.79, positive predictive values > 0.89 and relative errors < 11% for ICD and < 8.5% for CAD. The best identified SL algorithm showed lower performances than other DMOs (absolute error < 0.21 m). Lower performances across all DMOs were found for the cohort with most severe gait impairments (proximal femoral fracture). Algorithms' performances were lower for short walking bouts; slower gait speeds (< 0.5 m/s) resulted in reduced performance of the CAD and SL algorithms. CONCLUSIONS Overall, the identified algorithms enabled a robust estimation of key DMOs. Our findings showed that the choice of algorithm for estimation of gait sequence detection and CAD should be cohort-specific (e.g., slow walkers and with gait impairments). Short walking bout length and slow walking speed worsened algorithms' performances. Trial registration ISRCTN - 12246987.
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Affiliation(s)
- M Encarna Micó-Amigo
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - Tecla Bonci
- Department of Mechanical Engineering and Insigneo Institute for in Silico Medicine, The University of Sheffield, Sheffield, UK
| | - Anisoara Paraschiv-Ionescu
- Laboratory of Movement Analysis and Measurement, Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland
| | - Martin Ullrich
- Machine Learning and Data Analytics Lab, Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Cameron Kirk
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - Abolfazl Soltani
- Laboratory of Movement Analysis and Measurement, Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland
| | - Arne Küderle
- Machine Learning and Data Analytics Lab, Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Eran Gazit
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Francesca Salis
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
- Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy
| | - Lisa Alcock
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
- National Institute for Health and Care Research (NIHR) Newcastle Biomedical Research Centre (BRC), Newcastle University and The Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK
| | - Kamiar Aminian
- Laboratory of Movement Analysis and Measurement, Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland
| | - Clemens Becker
- Robert Bosch Gesellschaft für Medizinische Forschung, Stuttgart, Germany
| | - Stefano Bertuletti
- Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy
| | - Philip Brown
- The Newcastle Upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK
| | - Ellen Buckley
- Department of Mechanical Engineering and Insigneo Institute for in Silico Medicine, The University of Sheffield, Sheffield, UK
| | - Alma Cantu
- School of Computing, Newcastle University, Newcastle upon Tyne, UK
| | - Anne-Elie Carsin
- Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Catalonia, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Marco Caruso
- Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy
| | - Brian Caulfield
- Insight Centre for Data Analytics, University College Dublin, Dublin, Ireland
- School of Public Health, Physiotherapy and Sports Science, University College Dublin, Dublin, Ireland
| | - Andrea Cereatti
- Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy
| | - Lorenzo Chiari
- Department of Electrical, Electronic and Information Engineering «Guglielmo Marconi», University of Bologna, Bologna, Italy
- Health Sciences and Technologies-Interdepartmental Center for Industrial Research (CIRI-SDV), University of Bologna, Bologna, Italy
| | - Ilaria D'Ascanio
- Department of Electrical, Electronic and Information Engineering «Guglielmo Marconi», University of Bologna, Bologna, Italy
| | - Bjoern Eskofier
- Machine Learning and Data Analytics Lab, Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Sara Fernstad
- School of Computing, Newcastle University, Newcastle upon Tyne, UK
| | | | - Judith Garcia-Aymerich
- Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Catalonia, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Clint Hansen
- Department of Neurology, University Medical Center Schleswig-Holstein Campus Kiel, Kiel, Germany
| | - Jeffrey M Hausdorff
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
- Sagol School of Neuroscience and Department of Physical Therapy, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
- Rush Alzheimer's Disease Center and Department of Orthopaedic Surgery, Rush University Medical Center, Chicago, IL, USA
| | - Hugo Hiden
- School of Computing, Newcastle University, Newcastle upon Tyne, UK
| | - Emily Hume
- Department of Sport, Exercise and Rehabilitation, Northumbria University Newcastle, Newcastle upon Tyne, UK
| | - Alison Keogh
- Insight Centre for Data Analytics, University College Dublin, Dublin, Ireland
- School of Public Health, Physiotherapy and Sports Science, University College Dublin, Dublin, Ireland
| | - Felix Kluge
- Machine Learning and Data Analytics Lab, Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
- Novartis Institutes of Biomedical Research, Novartis Pharma AG, Basel, Switzerland
| | - Sarah Koch
- Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Catalonia, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Walter Maetzler
- Department of Neurology, University Medical Center Schleswig-Holstein Campus Kiel, Kiel, Germany
| | - Dimitrios Megaritis
- Department of Sport, Exercise and Rehabilitation, Northumbria University Newcastle, Newcastle upon Tyne, UK
| | - Arne Mueller
- Novartis Institutes of Biomedical Research, Novartis Pharma AG, Basel, Switzerland
| | | | - Luca Palmerini
- Department of Electrical, Electronic and Information Engineering «Guglielmo Marconi», University of Bologna, Bologna, Italy
- Health Sciences and Technologies-Interdepartmental Center for Industrial Research (CIRI-SDV), University of Bologna, Bologna, Italy
| | - Lars Schwickert
- Robert Bosch Gesellschaft für Medizinische Forschung, Stuttgart, Germany
| | - Kirsty Scott
- Department of Mechanical Engineering and Insigneo Institute for in Silico Medicine, The University of Sheffield, Sheffield, UK
| | - Basil Sharrack
- Department of Neuroscience and Sheffield NIHR Translational Neuroscience BRC, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK
| | | | - David Singleton
- Insight Centre for Data Analytics, University College Dublin, Dublin, Ireland
- School of Public Health, Physiotherapy and Sports Science, University College Dublin, Dublin, Ireland
| | - Beatrix Vereijken
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, Trondheim, Norway
| | - Ioannis Vogiatzis
- Department of Sport, Exercise and Rehabilitation, Northumbria University Newcastle, Newcastle upon Tyne, UK
| | - Alison J Yarnall
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
- National Institute for Health and Care Research (NIHR) Newcastle Biomedical Research Centre (BRC), Newcastle University and The Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK
- The Newcastle Upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK
| | - Lynn Rochester
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
- National Institute for Health and Care Research (NIHR) Newcastle Biomedical Research Centre (BRC), Newcastle University and The Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK
- The Newcastle Upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK
| | - Claudia Mazzà
- Department of Mechanical Engineering and Insigneo Institute for in Silico Medicine, The University of Sheffield, Sheffield, UK
| | - Silvia Del Din
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK.
- National Institute for Health and Care Research (NIHR) Newcastle Biomedical Research Centre (BRC), Newcastle University and The Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK.
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8
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Salis F, Bertuletti S, Bonci T, Caruso M, Scott K, Alcock L, Buckley E, Gazit E, Hansen C, Schwickert L, Aminian K, Becker C, Brown P, Carsin AE, Caulfield B, Chiari L, D’Ascanio I, Del Din S, Eskofier BM, Garcia-Aymerich J, Hausdorff JM, Hume EC, Kirk C, Kluge F, Koch S, Kuederle A, Maetzler W, Micó-Amigo EM, Mueller A, Neatrour I, Paraschiv-Ionescu A, Palmerini L, Yarnall AJ, Rochester L, Sharrack B, Singleton D, Vereijken B, Vogiatzis I, Della Croce U, Mazzà C, Cereatti A. A multi-sensor wearable system for the assessment of diseased gait in real-world conditions. Front Bioeng Biotechnol 2023; 11:1143248. [PMID: 37214281 PMCID: PMC10194657 DOI: 10.3389/fbioe.2023.1143248] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Accepted: 03/30/2023] [Indexed: 05/24/2023] Open
Abstract
Introduction: Accurately assessing people's gait, especially in real-world conditions and in case of impaired mobility, is still a challenge due to intrinsic and extrinsic factors resulting in gait complexity. To improve the estimation of gait-related digital mobility outcomes (DMOs) in real-world scenarios, this study presents a wearable multi-sensor system (INDIP), integrating complementary sensing approaches (two plantar pressure insoles, three inertial units and two distance sensors). Methods: The INDIP technical validity was assessed against stereophotogrammetry during a laboratory experimental protocol comprising structured tests (including continuous curvilinear and rectilinear walking and steps) and a simulation of daily-life activities (including intermittent gait and short walking bouts). To evaluate its performance on various gait patterns, data were collected on 128 participants from seven cohorts: healthy young and older adults, patients with Parkinson's disease, multiple sclerosis, chronic obstructive pulmonary disease, congestive heart failure, and proximal femur fracture. Moreover, INDIP usability was evaluated by recording 2.5-h of real-world unsupervised activity. Results and discussion: Excellent absolute agreement (ICC >0.95) and very limited mean absolute errors were observed for all cohorts and digital mobility outcomes (cadence ≤0.61 steps/min, stride length ≤0.02 m, walking speed ≤0.02 m/s) in the structured tests. Larger, but limited, errors were observed during the daily-life simulation (cadence 2.72-4.87 steps/min, stride length 0.04-0.06 m, walking speed 0.03-0.05 m/s). Neither major technical nor usability issues were declared during the 2.5-h acquisitions. Therefore, the INDIP system can be considered a valid and feasible solution to collect reference data for analyzing gait in real-world conditions.
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Affiliation(s)
- Francesca Salis
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
- Interuniversity Centre of Bioengineering of the Human Neuromusculoskeletal System (IuC BoHNes), Sassari, Italy
| | - Stefano Bertuletti
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
- Interuniversity Centre of Bioengineering of the Human Neuromusculoskeletal System (IuC BoHNes), Sassari, Italy
| | - Tecla Bonci
- Department of Mechanical Engineering, Insigneo Institute for In Silico Medicine, The University of Sheffield, Sheffield, United Kingdom
| | - Marco Caruso
- Interuniversity Centre of Bioengineering of the Human Neuromusculoskeletal System (IuC BoHNes), Sassari, Italy
- Department of Electronics and Telecommunications, Politecnico Di Torino, Torino, Italy
| | - Kirsty Scott
- Department of Mechanical Engineering, Insigneo Institute for In Silico Medicine, The University of Sheffield, Sheffield, United Kingdom
| | - Lisa Alcock
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne, United Kingdom
- National Institute for Health and Care Research (NIHR) Newcastle Biomedical Research Centre (BRC), Newcastle University, Newcastle Upon Tyne, United Kingdom
| | - Ellen Buckley
- Department of Mechanical Engineering, Insigneo Institute for In Silico Medicine, The University of Sheffield, Sheffield, United Kingdom
| | - Eran Gazit
- Centre for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Centre, Tel Aviv, Israel
| | - Clint Hansen
- Department of Neurology, University Medical Centre Schleswig-Holstein Campus Kiel and Kiel University, Kiel, Germany
| | - Lars Schwickert
- Department for Geriatric Rehabilitation, Robert-Bosch-Hospital, Stuttgart, Germany
| | - Kamiar Aminian
- Laboratory of Movement Analysis and Measurement, Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland
| | - Clemens Becker
- Department for Geriatric Rehabilitation, Robert-Bosch-Hospital, Stuttgart, Germany
| | - Philip Brown
- Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle Upon Tyne, United Kingdom
| | - Anne-Elie Carsin
- Instituto de Salud Global Barcelona, Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain
- Faculty of Health and Life Sciences, Universitat Pompeu Fabra, Barcelona, Spain
- CIBER Epidemiología y Salud Pública, Madrid, Spain
| | - Brian Caulfield
- Insight Centre for Data Analytics, University College Dublin, Dublin, Ireland
| | - Lorenzo Chiari
- Department of Electrical, Electronic and Information Engineering “Guglielmo Marconi”, University of Bologna, Bologna, Italy
- Health Sciences and Technologies-Interdepartmental Centre for Industrial Research (CIRI-SDV), University of Bologna, Bologna, Italy
| | - Ilaria D’Ascanio
- Department of Electrical, Electronic and Information Engineering “Guglielmo Marconi”, University of Bologna, Bologna, Italy
| | - Silvia Del Din
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne, United Kingdom
- National Institute for Health and Care Research (NIHR) Newcastle Biomedical Research Centre (BRC), Newcastle University, Newcastle Upon Tyne, United Kingdom
| | - Bjoern M. Eskofier
- Machine Learning and Data Analytics Lab, Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Judith Garcia-Aymerich
- Instituto de Salud Global Barcelona, Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain
- Faculty of Health and Life Sciences, Universitat Pompeu Fabra, Barcelona, Spain
- CIBER Epidemiología y Salud Pública, Madrid, Spain
| | - Jeffrey M. Hausdorff
- Centre for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Centre, Tel Aviv, Israel
| | - Emily C. Hume
- Department of Sport, Exercise and Rehabilitation, Faculty of Health and Life Sciences, Northumbria University, Northumbia, United Kingdom
| | - Cameron Kirk
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne, United Kingdom
| | - Felix Kluge
- Machine Learning and Data Analytics Lab, Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
- Novartis Institutes of Biomedical Research, Novartis Pharma AG, Basel, Switzerland
| | - Sarah Koch
- Instituto de Salud Global Barcelona, Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain
- Faculty of Health and Life Sciences, Universitat Pompeu Fabra, Barcelona, Spain
- CIBER Epidemiología y Salud Pública, Madrid, Spain
| | - Arne Kuederle
- Machine Learning and Data Analytics Lab, Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Walter Maetzler
- Department of Neurology, University Medical Centre Schleswig-Holstein Campus Kiel and Kiel University, Kiel, Germany
| | - Encarna M. Micó-Amigo
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne, United Kingdom
| | - Arne Mueller
- Novartis Institutes of Biomedical Research, Novartis Pharma AG, Basel, Switzerland
| | - Isabel Neatrour
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne, United Kingdom
| | - Anisoara Paraschiv-Ionescu
- Laboratory of Movement Analysis and Measurement, Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland
| | - Luca Palmerini
- Department of Electrical, Electronic and Information Engineering “Guglielmo Marconi”, University of Bologna, Bologna, Italy
- Health Sciences and Technologies-Interdepartmental Centre for Industrial Research (CIRI-SDV), University of Bologna, Bologna, Italy
| | - Alison J. Yarnall
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne, United Kingdom
- National Institute for Health and Care Research (NIHR) Newcastle Biomedical Research Centre (BRC), Newcastle University, Newcastle Upon Tyne, United Kingdom
- Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle Upon Tyne, United Kingdom
| | - Lynn Rochester
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne, United Kingdom
- National Institute for Health and Care Research (NIHR) Newcastle Biomedical Research Centre (BRC), Newcastle University, Newcastle Upon Tyne, United Kingdom
- Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle Upon Tyne, United Kingdom
| | - Basil Sharrack
- Department of Neuroscience and Sheffield NIHR Translational Neuroscience BRC, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, United Kingdom
| | - David Singleton
- Insight Centre for Data Analytics, University College Dublin, Dublin, Ireland
| | - Beatrix Vereijken
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, Trondheim, Norway
| | - Ioannis Vogiatzis
- Department of Sport, Exercise and Rehabilitation, Faculty of Health and Life Sciences, Northumbria University, Northumbia, United Kingdom
| | - Ugo Della Croce
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
- Interuniversity Centre of Bioengineering of the Human Neuromusculoskeletal System (IuC BoHNes), Sassari, Italy
| | - Claudia Mazzà
- Department of Mechanical Engineering, Insigneo Institute for In Silico Medicine, The University of Sheffield, Sheffield, United Kingdom
| | - Andrea Cereatti
- Interuniversity Centre of Bioengineering of the Human Neuromusculoskeletal System (IuC BoHNes), Sassari, Italy
- Department of Electronics and Telecommunications, Politecnico Di Torino, Torino, Italy
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9
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da Rosa Tavares JE, Ullrich M, Roth N, Kluge F, Eskofier BM, Gaßner H, Klucken J, Gladow T, Marxreiter F, da Costa CA, da Rosa Righi R, Victória Barbosa JL. uTUG: An unsupervised Timed Up and Go test for Parkinson’s disease. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104394] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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10
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Delgado-Ortiz L, Polhemus A, Keogh A, Sutton N, Remmele W, Hansen C, Kluge F, Sharrack B, Becker C, Troosters T, Maetzler W, Rochester L, Frei A, Puhan MA, Garcia-Aymerich J. Listening to the patients' voice: a conceptual framework of the walking experience. Age Ageing 2023; 52:7008636. [PMID: 36729471 PMCID: PMC9894103 DOI: 10.1093/ageing/afac233] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Revised: 06/27/2022] [Indexed: 02/03/2023] Open
Abstract
BACKGROUND walking is crucial for an active and healthy ageing, but the perspectives of individuals living with walking impairment are still poorly understood. OBJECTIVES to identify and synthesise evidence describing walking as experienced by adults living with mobility-impairing health conditions and to propose an empirical conceptual framework of walking experience. METHODS we performed a systematic review and meta-ethnography of qualitative evidence, searching seven electronic databases for records that explored personal experiences of walking in individuals living with conditions of diverse aetiology. Conditions included Parkinson's disease, multiple sclerosis, chronic obstructive pulmonary disease, hip fracture, heart failure, frailty and sarcopenia. Data were extracted, critically appraised using the NICE quality checklist and synthesised using standardised best practices. RESULTS from 2,552 unique records, 117 were eligible. Walking experience was similar across conditions and described by seven themes: (i) becoming aware of the personal walking experience, (ii) the walking experience as a link between individuals' activities and sense of self, (iii) the physical walking experience, (iv) the mental and emotional walking experience, (v) the social walking experience, (vi) the context of the walking experience and (vii) behavioural and attitudinal adaptations resulting from the walking experience. We propose a novel conceptual framework that visually represents the walking experience, informed by the interplay between these themes. CONCLUSION a multi-faceted and dynamic experience of walking was common across health conditions. Our conceptual framework of the walking experience provides a novel theoretical structure for patient-centred clinical practice, research and public health.
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Affiliation(s)
| | | | - Alison Keogh
- Insight Centre for Data Analytics, University College Dublin, Dublin, Ireland
| | | | | | - Clint Hansen
- Department of Neurology, University Medical Center Schleswig-Holstein, Kiel, Germany
| | - Felix Kluge
- Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Basil Sharrack
- Department of Neuroscience and Sheffield NIHR Translational Neuroscience BRC, Sheffield Teaching Hospitals NHS Foundation Trust & University of Sheffield, Sheffield, UK
| | - Clemens Becker
- Department of Clinical Gerontology, Robert-Bosch-Hospital, Stuttgart, Germany
| | - Thierry Troosters
- Department of Rehabilitation Sciences, KU Leuven, Leuven, Belgium,Department of Respiratory Diseases, University Hospitals Leuven, Leuven, Belgium
| | - Walter Maetzler
- Department of Neurology, University Medical Center Schleswig-Holstein, Kiel, Germany
| | - Lynn Rochester
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - Anja Frei
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland
| | - Milo A Puhan
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland
| | - Judith Garcia-Aymerich
- Address correspondence to: J. Garcia-Aymerich, ISGlobal, Dr. Aiguader 88, PRBB. Barcelona, Spain. Tel: (+34) 93 214 73 80;
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11
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Palmerini L, Reggi L, Bonci T, Del Din S, Micó-Amigo ME, Salis F, Bertuletti S, Caruso M, Cereatti A, Gazit E, Paraschiv-Ionescu A, Soltani A, Kluge F, Küderle A, Ullrich M, Kirk C, Hiden H, D’Ascanio I, Hansen C, Rochester L, Mazzà C, Chiari L. Mobility recorded by wearable devices and gold standards: the Mobilise-D procedure for data standardization. Sci Data 2023; 10:38. [PMID: 36658136 PMCID: PMC9852581 DOI: 10.1038/s41597-023-01930-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Accepted: 01/03/2023] [Indexed: 01/21/2023] Open
Abstract
Wearable devices are used in movement analysis and physical activity research to extract clinically relevant information about an individual's mobility. Still, heterogeneity in protocols, sensor characteristics, data formats, and gold standards represent a barrier for data sharing, reproducibility, and external validation. In this study, we aim at providing an example of how movement data (from the real-world and the laboratory) recorded from different wearables and gold standard technologies can be organized, integrated, and stored. We leveraged on our experience from a large multi-centric study (Mobilise-D) to provide guidelines that can prove useful to access, understand, and re-use the data that will be made available from the study. These guidelines highlight the encountered challenges and the adopted solutions with the final aim of supporting standardization and integration of data in other studies and, in turn, to increase and facilitate comparison of data recorded in the scientific community. We also provide samples of standardized data, so that both the structure of the data and the procedure can be easily understood and reproduced.
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Affiliation(s)
- Luca Palmerini
- grid.6292.f0000 0004 1757 1758University of Bologna, Department of Electrical, Electronic and Information Engineering ‘Guglielmo Marconi’, Bologna, Italy ,grid.6292.f0000 0004 1757 1758University of Bologna, Health Sciences and Technologies—Interdepartmental Center for Industrial Research (CIRI-SDV), Bologna, Italy
| | - Luca Reggi
- grid.6292.f0000 0004 1757 1758University of Bologna, Health Sciences and Technologies—Interdepartmental Center for Industrial Research (CIRI-SDV), Bologna, Italy
| | - Tecla Bonci
- grid.11835.3e0000 0004 1936 9262The University of Sheffield, INSIGNEO Institute for in silico Medicine, Sheffield, UK ,grid.11835.3e0000 0004 1936 9262The University of Sheffield, Department of Mechanical Engineering, Sheffield, UK
| | - Silvia Del Din
- grid.1006.70000 0001 0462 7212Newcastle University, Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle, UK
| | - M. Encarna Micó-Amigo
- grid.1006.70000 0001 0462 7212Newcastle University, Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle, UK
| | - Francesca Salis
- grid.11450.310000 0001 2097 9138University of Sassari, Department of Biomedical Sciences, Sassari, Italy
| | - Stefano Bertuletti
- grid.11450.310000 0001 2097 9138University of Sassari, Department of Biomedical Sciences, Sassari, Italy
| | - Marco Caruso
- grid.4800.c0000 0004 1937 0343Politecnico di Torino, Department of Electronics and Telecommunications, Torino, Italy ,grid.4800.c0000 0004 1937 0343Politecnico di Torino, PolitoBIOMed Lab – Biomedical Engineering Lab, Torino, Italy
| | - Andrea Cereatti
- grid.4800.c0000 0004 1937 0343Politecnico di Torino, Department of Electronics and Telecommunications, Torino, Italy
| | - Eran Gazit
- grid.413449.f0000 0001 0518 6922Tel Aviv Sourasky Medical Center, Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv-Yafo, Israel
| | - Anisoara Paraschiv-Ionescu
- grid.5333.60000000121839049Laboratory of Movement Analysis and Measurement, Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland
| | - Abolfazl Soltani
- grid.5333.60000000121839049Laboratory of Movement Analysis and Measurement, Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland
| | - Felix Kluge
- grid.5330.50000 0001 2107 3311Machine Learning and Data Analytics Lab, Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-University Erlangen-Nürnberg, Erlangen, Germany
| | - Arne Küderle
- grid.5330.50000 0001 2107 3311Machine Learning and Data Analytics Lab, Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-University Erlangen-Nürnberg, Erlangen, Germany
| | - Martin Ullrich
- grid.5330.50000 0001 2107 3311Machine Learning and Data Analytics Lab, Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-University Erlangen-Nürnberg, Erlangen, Germany
| | - Cameron Kirk
- grid.1006.70000 0001 0462 7212Newcastle University, Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle, UK
| | - Hugo Hiden
- grid.1006.70000 0001 0462 7212Newcastle University, School of Computing, Newcastle, UK
| | - Ilaria D’Ascanio
- grid.6292.f0000 0004 1757 1758University of Bologna, Department of Electrical, Electronic and Information Engineering ‘Guglielmo Marconi’, Bologna, Italy
| | - Clint Hansen
- grid.412468.d0000 0004 0646 2097Neurogeriatrics Kiel, Department of Neurology, University Hospital Schleswig-Holstein, Kiel, Germany
| | - Lynn Rochester
- grid.1006.70000 0001 0462 7212Newcastle University, Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle, UK ,The Newcastle upon Tyne NHS Foundation Trust, Newcastle, UK
| | - Claudia Mazzà
- grid.11835.3e0000 0004 1936 9262The University of Sheffield, INSIGNEO Institute for in silico Medicine, Sheffield, UK ,grid.11835.3e0000 0004 1936 9262The University of Sheffield, Department of Mechanical Engineering, Sheffield, UK
| | - Lorenzo Chiari
- grid.6292.f0000 0004 1757 1758University of Bologna, Department of Electrical, Electronic and Information Engineering ‘Guglielmo Marconi’, Bologna, Italy ,grid.6292.f0000 0004 1757 1758University of Bologna, Health Sciences and Technologies—Interdepartmental Center for Industrial Research (CIRI-SDV), Bologna, Italy
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12
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Scott K, Bonci T, Salis F, Alcock L, Buckley E, Gazit E, Hansen C, Schwickert L, Aminian K, Bertuletti S, Caruso M, Chiari L, Sharrack B, Maetzler W, Becker C, Hausdorff JM, Vogiatzis I, Brown P, Del Din S, Eskofier B, Paraschiv-Ionescu A, Keogh A, Kirk C, Kluge F, Micó-Amigo EM, Mueller A, Neatrour I, Niessen M, Palmerini L, Sillen H, Singleton D, Ullrich M, Vereijken B, Froehlich M, Brittain G, Caulfield B, Koch S, Carsin AE, Garcia-Aymerich J, Kuederle A, Yarnall A, Rochester L, Cereatti A, Mazzà C. Design and validation of a multi-task, multi-context protocol for real-world gait simulation. J Neuroeng Rehabil 2022; 19:141. [PMID: 36522646 PMCID: PMC9754996 DOI: 10.1186/s12984-022-01116-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Accepted: 11/23/2022] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Measuring mobility in daily life entails dealing with confounding factors arising from multiple sources, including pathological characteristics, patient specific walking strategies, environment/context, and purpose of the task. The primary aim of this study is to propose and validate a protocol for simulating real-world gait accounting for all these factors within a single set of observations, while ensuring minimisation of participant burden and safety. METHODS The protocol included eight motor tasks at varying speed, incline/steps, surface, path shape, cognitive demand, and included postures that may abruptly alter the participants' strategy of walking. It was deployed in a convenience sample of 108 participants recruited from six cohorts that included older healthy adults (HA) and participants with potentially altered mobility due to Parkinson's disease (PD), multiple sclerosis (MS), proximal femoral fracture (PFF), chronic obstructive pulmonary disease (COPD) or congestive heart failure (CHF). A novelty introduced in the protocol was the tiered approach to increase difficulty both within the same task (e.g., by allowing use of aids or armrests) and across tasks. RESULTS The protocol proved to be safe and feasible (all participants could complete it and no adverse events were recorded) and the addition of the more complex tasks allowed a much greater spread in walking speeds to be achieved compared to standard straight walking trials. Furthermore, it allowed a representation of a variety of daily life relevant mobility aspects and can therefore be used for the validation of monitoring devices used in real life. CONCLUSIONS The protocol allowed for measuring gait in a variety of pathological conditions suggests that it can also be used to detect changes in gait due to, for example, the onset or progression of a disease, or due to therapy. TRIAL REGISTRATION ISRCTN-12246987.
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Affiliation(s)
- Kirsty Scott
- Department of Mechanical Engineering and Insigneo Institute for in Silico Medicine, The University of Sheffield, Sheffield, UK. .,Department of Mechanical Engineering, The University of Sheffield, Sheffield, UK.
| | - Tecla Bonci
- Department of Mechanical Engineering and Insigneo Institute for in Silico Medicine, The University of Sheffield, Sheffield, UK.,Department of Mechanical Engineering, The University of Sheffield, Sheffield, UK
| | - Francesca Salis
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
| | - Lisa Alcock
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - Ellen Buckley
- Department of Mechanical Engineering and Insigneo Institute for in Silico Medicine, The University of Sheffield, Sheffield, UK.,Department of Mechanical Engineering, The University of Sheffield, Sheffield, UK
| | - Eran Gazit
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Clint Hansen
- Department of Neurology, University Medical Center Schleswig-Holstein Campus Kiel, Kiel, Germany
| | - Lars Schwickert
- Robert Bosch Gesellschaft für Medizinische Forschung, Stuttgart, Germany
| | - Kamiar Aminian
- Laboratory of Movement Analysis and Measurement, Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland
| | - Stefano Bertuletti
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
| | - Marco Caruso
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy.,Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy.,PolitoBIOMed Lab, Biomedical Engineering Lab, Politecnico di Torino, Turin, Italy
| | - Lorenzo Chiari
- Department of Electrical, Electronic and Information Engineering «Guglielmo Marconi», University of Bologna, Bologna, Italy.,Health Sciences and Technologies-Interdepartmental Center for Industrial Research (CIRI-SDV), University of Bologna, Bologna, Italy
| | - Basil Sharrack
- Department of Neuroscience and Sheffield NIHR Translational Neuroscience BRC, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK
| | - Walter Maetzler
- Department of Neurology, University Medical Center Schleswig-Holstein Campus Kiel, Kiel, Germany
| | - Clemens Becker
- Robert Bosch Gesellschaft für Medizinische Forschung, Stuttgart, Germany
| | - Jeffrey M Hausdorff
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Ioannis Vogiatzis
- Department of Sport, Exercise and Rehabilitation, Northumbria University Newcastle, Newcastle upon Tyne, UK
| | - Philip Brown
- Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK
| | - Silvia Del Din
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - Björn Eskofier
- Machine Learning and Data Analytics Lab, Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Anisoara Paraschiv-Ionescu
- Laboratory of Movement Analysis and Measurement, Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland
| | - Alison Keogh
- Insight Centre for Data Analytics, University College Dublin, Dublin, Ireland.,School of Public Health, Physiotherapy and Sports Science, University College Dublin, Dublin, Ireland
| | - Cameron Kirk
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - Felix Kluge
- Machine Learning and Data Analytics Lab, Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.,Novartis Institutes of Biomedical Research, Novartis Pharma AG, Basel, Switzerland
| | - Encarna M Micó-Amigo
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - Arne Mueller
- Novartis Institutes of Biomedical Research, Novartis Pharma AG, Basel, Switzerland
| | - Isabel Neatrour
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | | | - Luca Palmerini
- Department of Electrical, Electronic and Information Engineering «Guglielmo Marconi», University of Bologna, Bologna, Italy.,Health Sciences and Technologies-Interdepartmental Center for Industrial Research (CIRI-SDV), University of Bologna, Bologna, Italy
| | | | - David Singleton
- Insight Centre for Data Analytics, University College Dublin, Dublin, Ireland.,School of Public Health, Physiotherapy and Sports Science, University College Dublin, Dublin, Ireland
| | - Martin Ullrich
- Machine Learning and Data Analytics Lab, Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Beatrix Vereijken
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, Trondheim, Norway
| | | | - Gavin Brittain
- Department of Neuroscience and Sheffield NIHR Translational Neuroscience BRC, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK
| | - Brian Caulfield
- Insight Centre for Data Analytics, University College Dublin, Dublin, Ireland.,School of Public Health, Physiotherapy and Sports Science, University College Dublin, Dublin, Ireland
| | - Sarah Koch
- Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain.,Universitat Pompeu Fabra, Barcelona, Catalonia, Spain.,CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Anne-Elie Carsin
- Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain.,Universitat Pompeu Fabra, Barcelona, Catalonia, Spain.,CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Judith Garcia-Aymerich
- Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain.,Universitat Pompeu Fabra, Barcelona, Catalonia, Spain.,CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Arne Kuederle
- Machine Learning and Data Analytics Lab, Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Alison Yarnall
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK.,Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK
| | - Lynn Rochester
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK.,Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK
| | - Andrea Cereatti
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy.,Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy.,PolitoBIOMed Lab, Biomedical Engineering Lab, Politecnico di Torino, Turin, Italy
| | - Claudia Mazzà
- Department of Mechanical Engineering and Insigneo Institute for in Silico Medicine, The University of Sheffield, Sheffield, UK.,Department of Mechanical Engineering, The University of Sheffield, Sheffield, UK
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13
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Ollenschläger M, Küderle A, Mehringer W, Seifer AK, Winkler J, Gaßner H, Kluge F, Eskofier BM. MaD GUI: An Open-Source Python Package for Annotation and Analysis of Time-Series Data. Sensors (Basel) 2022; 22:5849. [PMID: 35957406 PMCID: PMC9371110 DOI: 10.3390/s22155849] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 07/17/2022] [Accepted: 08/03/2022] [Indexed: 06/15/2023]
Abstract
Developing machine learning algorithms for time-series data often requires manual annotation of the data. To do so, graphical user interfaces (GUIs) are an important component. Existing Python packages for annotation and analysis of time-series data have been developed without addressing adaptability, usability, and user experience. Therefore, we developed a generic open-source Python package focusing on adaptability, usability, and user experience. The developed package, Machine Learning and Data Analytics (MaD) GUI, enables developers to rapidly create a GUI for their specific use case. Furthermore, MaD GUI enables domain experts without programming knowledge to annotate time-series data and apply algorithms to it. We conducted a small-scale study with participants from three international universities to test the adaptability of MaD GUI by developers and to test the user interface by clinicians as representatives of domain experts. MaD GUI saves up to 75% of time in contrast to using a state-of-the-art package. In line with this, subjective ratings regarding usability and user experience show that MaD GUI is preferred over a state-of-the-art package by developers and clinicians. MaD GUI reduces the effort of developers in creating GUIs for time-series analysis and offers similar usability and user experience for clinicians as a state-of-the-art package.
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Affiliation(s)
- Malte Ollenschläger
- Machine Learning and Data Analytics Lab, Department of Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91052 Erlangen, Germany
- Department of Molecular Neurology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91054 Erlangen, Germany
| | - Arne Küderle
- Machine Learning and Data Analytics Lab, Department of Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91052 Erlangen, Germany
| | - Wolfgang Mehringer
- Machine Learning and Data Analytics Lab, Department of Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91052 Erlangen, Germany
| | - Ann-Kristin Seifer
- Machine Learning and Data Analytics Lab, Department of Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91052 Erlangen, Germany
| | - Jürgen Winkler
- Department of Molecular Neurology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91054 Erlangen, Germany
| | - Heiko Gaßner
- Department of Molecular Neurology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91054 Erlangen, Germany
- Fraunhofer IIS, Fraunhofer Institute for Integrated Circuits IIS, 91058 Erlangen, Germany
| | - Felix Kluge
- Machine Learning and Data Analytics Lab, Department of Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91052 Erlangen, Germany
| | - Bjoern M. Eskofier
- Machine Learning and Data Analytics Lab, Department of Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91052 Erlangen, Germany
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14
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Knitza J, Janousek L, Kluge F, von der Decken CB, Kleinert S, Vorbrüggen W, Kleyer A, Simon D, Hueber AJ, Muehlensiepen F, Vuillerme N, Schett G, Eskofier BM, Welcker M, Bartz-Bazzanella P. Machine learning-based improvement of an online rheumatology referral and triage system. Front Med (Lausanne) 2022; 9:954056. [PMID: 35935756 PMCID: PMC9354580 DOI: 10.3389/fmed.2022.954056] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Accepted: 06/30/2022] [Indexed: 11/23/2022] Open
Abstract
Introduction Rheport is an online rheumatology referral system allowing automatic appointment triaging of new rheumatology patient referrals according to the respective probability of an inflammatory rheumatic disease (IRD). Previous research reported that Rheport was well accepted among IRD patients. Its accuracy was, however, limited, currently being based on an expert-based weighted sum score. This study aimed to evaluate whether machine learning (ML) models could improve this limited accuracy. Materials and methods Data from a national rheumatology registry (RHADAR) was used to train and test nine different ML models to correctly classify IRD patients. Diagnostic performance was compared of ML models and the current algorithm was compared using the area under the receiver operating curve (AUROC). Feature importance was investigated using shapley additive explanation (SHAP). Results A complete data set of 2265 patients was used to train and test ML models. 30.5% of patients were diagnosed with an IRD, 69.3% were female. The diagnostic accuracy of the current Rheport algorithm (AUROC of 0.534) could be improved with all ML models, (AUROC ranging between 0.630 and 0.737). Targeting a sensitivity of 90%, the logistic regression model could double current specificity (17% vs. 33%). Finger joint pain, inflammatory marker levels, psoriasis, symptom duration and female sex were the five most important features of the best performing logistic regression model for IRD classification. Conclusion In summary, ML could improve the accuracy of a currently used rheumatology online referral system. Including further laboratory parameters and enabling individual feature importance adaption could increase accuracy and lead to broader usage.
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Affiliation(s)
- Johannes Knitza
- Department of Internal Medicine 3, Friedrich-Alexander-University Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
- Deutsches Zentrum für Immuntherapie (DZI), Friedrich-Alexander-University Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
- Université Grenoble Alpes, AGEIS, Grenoble, France
- *Correspondence: Johannes Knitza,
| | - Lena Janousek
- Machine Learning and Data Analytics Lab, Department of Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Felix Kluge
- Machine Learning and Data Analytics Lab, Department of Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Cay Benedikt von der Decken
- Medizinisches Versorgungszentrum Stolberg, Stolberg, Germany
- Klinik für Internistische Rheumatologie, Rhein-Maas-Klinikum, Würselen, Germany
- RheumaDatenRhePort (rhadar), Planegg, Germany
| | - Stefan Kleinert
- RheumaDatenRhePort (rhadar), Planegg, Germany
- Praxisgemeinschaft Rheumatologie-Nephrologie, Erlangen, Germany
- Medizinische Klinik 3, Rheumatology/Immunology, Universitätsklinikum Würzburg, Würzburg, Germany
| | - Wolfgang Vorbrüggen
- RheumaDatenRhePort (rhadar), Planegg, Germany
- Verein zur Förderung der Rheumatologie e.V., Würselen, Germany
| | - Arnd Kleyer
- Department of Internal Medicine 3, Friedrich-Alexander-University Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
- Deutsches Zentrum für Immuntherapie (DZI), Friedrich-Alexander-University Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
| | - David Simon
- Department of Internal Medicine 3, Friedrich-Alexander-University Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
- Deutsches Zentrum für Immuntherapie (DZI), Friedrich-Alexander-University Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
| | - Axel J. Hueber
- Department of Internal Medicine 3, Friedrich-Alexander-University Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
- Division of Rheumatology, Klinikum Nürnberg, Paracelsus Medical University, Nürnberg, Germany
| | - Felix Muehlensiepen
- Université Grenoble Alpes, AGEIS, Grenoble, France
- Faculty of Health Sciences, Center for Health Services Research, Brandenburg Medical School Theodor Fontane, Rüdersdorf, Germany
| | - Nicolas Vuillerme
- Université Grenoble Alpes, AGEIS, Grenoble, France
- Institut Universitaire de France, Paris, France
- LabCom Telecom4Health, Orange Labs and Univ. Grenoble Alpes, CNRS, Inria, Grenoble INP-UGA, Grenoble, France
| | - Georg Schett
- Department of Internal Medicine 3, Friedrich-Alexander-University Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
- Deutsches Zentrum für Immuntherapie (DZI), Friedrich-Alexander-University Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
| | - Bjoern M. Eskofier
- Machine Learning and Data Analytics Lab, Department of Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Martin Welcker
- RheumaDatenRhePort (rhadar), Planegg, Germany
- MVZ für Rheumatologie Dr. Martin Welcker GmbH, Planegg, Germany
| | - Peter Bartz-Bazzanella
- Klinik für Internistische Rheumatologie, Rhein-Maas-Klinikum, Würselen, Germany
- RheumaDatenRhePort (rhadar), Planegg, Germany
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15
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Roth N, Ullrich M, Kuderle A, Gladow T, Marxreiter F, Gassner H, Kluge F, Klucken J, Eskofier BM. Real-World Stair Ambulation Characteristics Differ Between Prospective Fallers and Non-Fallers in Parkinson's Disease. IEEE J Biomed Health Inform 2022; 26:4733-4742. [PMID: 35759602 DOI: 10.1109/jbhi.2022.3186766] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Falls are among the leading causes of injuries or death for individuals from the age of 65 and the prevalence of falls is especially high for patients suffering from neurological diseases like Parkinson's disease (PD). Due to advancements in wearable sensor technology, inertial measurement units (IMUs) can be integrated unobtrusively into patients' everyday lives to monitor various mobility and gait parameters, which are related to common risk factors like reduced balance and reduced lower-limb muscle strength, or lower range of joints. Although stair ambulation is a fundamental part of our daily lives and is known for its unique challenges for the gait and balance system, long-term gait analysis studies have not investigated real-world stair ambulation parameters yet. Therefore, we applied a recently published gait analysis pipeline on real-world foot-worn IMU data of 40 PD patients over a recording period of two weeks to extract objective gait parameters from level walking but also from stair ascending and stair descending gait. In combination with fall records from a prospective three-month follow-up phase, we investigated group differences in gait parameters of future fallers compared to non-fallers for each individual gait activity. We found significant differences in stair ascending and descending parameters. Stance time was increased by up to 20% and gait speed reduced by up to 16% for fallers compared to non-fallers during stair walking. These differences were not present in level walking parameters. Hence, these results suggest that real-world stair ambulation provides sensitive parameters for mobility and fall risk due to the unique challenges stairs add to the balance and control system. Our work complements existing gait analysis studies by adding new insights into mobility and gait performance during real-world gait.
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16
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Küderle A, Roth N, Zlatanovic J, Zrenner M, Eskofier B, Kluge F. The placement of foot-mounted IMU sensors does affect the accuracy of spatial parameters during regular walking. PLoS One 2022; 17:e0269567. [PMID: 35679231 PMCID: PMC9182246 DOI: 10.1371/journal.pone.0269567] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Accepted: 05/24/2022] [Indexed: 11/19/2022] Open
Abstract
Gait analysis using foot-worn inertial measurement units has proven to be a reliable tool to diagnose and monitor many neurological and musculoskeletal indications. However, only few studies have investigated the robustness of such systems to changes in the sensor attachment and no consensus for suitable sensor positions exists in the research community. Specifically for unsupervised real-world measurements, understanding how the reliability of the monitoring system changes when the sensor is attached differently is from high importance. In these scenarios, placement variations are expected because of user error or personal preferences. In this manuscript, we present the largest study to date comparing different sensor positions and attachments. We recorded 9000 strides with motion-capture reference from 14 healthy participants with six synchronized sensors attached at each foot. Spatial gait parameters were calculated using a double-integration method and compared to the reference system. The results indicate that relevant differences in the accuracy of the stride length exists between the sensor positions. While the average error over multiple strides is comparable, single stride errors and variability parameters differ greatly. We further present a physics model and an analysis of the raw sensor data to understand the origin of the observed differences. This analysis indicates that a variety of attachment parameters can influence the systems’ performance. While this is only the starting point to understand and mitigate these types of errors, we conclude that sensor systems and algorithms must be reevaluated when the sensor position or attachment changes.
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Affiliation(s)
- Arne Küderle
- Machine Learning and Data Analytics Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
- * E-mail:
| | - Nils Roth
- Machine Learning and Data Analytics Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Jovana Zlatanovic
- Machine Learning and Data Analytics Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Markus Zrenner
- Machine Learning and Data Analytics Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Bjoern Eskofier
- Machine Learning and Data Analytics Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Felix Kluge
- Machine Learning and Data Analytics Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
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17
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Ceron JD, López DM, Kluge F, Eskofier BM. Framework for Simultaneous Indoor Localization, Mapping, and Human Activity Recognition in Ambient Assisted Living Scenarios. Sensors (Basel) 2022; 22:3364. [PMID: 35591054 PMCID: PMC9101681 DOI: 10.3390/s22093364] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 04/07/2022] [Accepted: 04/21/2022] [Indexed: 06/15/2023]
Abstract
Indoor localization and human activity recognition are two important sources of information to provide context-based assistance. This information is relevant in ambient assisted living (AAL) scenarios, where older adults usually need supervision and assistance in their daily activities. However, indoor localization and human activity recognition have been mostly considered isolated problems. This work presents and evaluates a framework that takes advantage of the relationship between location and activity to simultaneously perform indoor localization, mapping, and human activity recognition. The proposed framework provides a non-intrusive configuration, which fuses data from an inertial measurement unit (IMU) placed in the person's shoe, with proximity and human activity-related data from Bluetooth low energy beacons (BLE) deployed in the indoor environment. A variant of the simultaneous location and mapping (SLAM) framework was used to fuse the location and human activity recognition (HAR) data. HAR was performed using data streaming algorithms. The framework was evaluated in a pilot study, using data from 22 people, 11 young people, and 11 older adults (people aged 65 years or older). As a result, seven activities of daily living were recognized with an F1 score of 88%, and the in-door location error was 0.98 ± 0.36 m for the young and 1.02 ± 0.24 m for the older adults. Furthermore, there were no significant differences between the groups, indicating that our proposed method works adequately in broad age ranges.
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Affiliation(s)
- Jesus D. Ceron
- Telematics Engineering Research Group, Telematics Department, Universidad Del Cauca (Unicauca), Popayán 190002, Colombia;
- Machine Learning and Data Analytics Lab, Computer Science Department, Friedrich-Alexander University, Erlangen-Nürnberg (FAU), 91052 Erlangen, Germany;
| | - Diego M. López
- Telematics Engineering Research Group, Telematics Department, Universidad Del Cauca (Unicauca), Popayán 190002, Colombia;
| | - Felix Kluge
- Machine Learning and Data Analytics Lab, Computer Science Department, Friedrich-Alexander University, Erlangen-Nürnberg (FAU), 91052 Erlangen, Germany;
| | - Bjoern M. Eskofier
- Machine Learning and Data Analytics Lab, Computer Science Department, Friedrich-Alexander University, Erlangen-Nürnberg (FAU), 91052 Erlangen, Germany;
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18
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Ollenschläger M, Kluge F, Müller-Schulz M, Püllen R, Möller C, Klucken J, Eskofier BM. Correction to: Wearable gait analysis systems: ready to be used by medical practitioners in geriatric wards? Eur Geriatr Med 2022; 13:825-826. [PMID: 35414052 PMCID: PMC9378333 DOI: 10.1007/s41999-022-00646-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Affiliation(s)
- Malte Ollenschläger
- Machine Learning and Data Analytics Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Carl-Thiersch-Str. 2b, 91052, Erlangen, Germany.
| | - Felix Kluge
- Machine Learning and Data Analytics Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Carl-Thiersch-Str. 2b, 91052, Erlangen, Germany
| | | | - Rupert Püllen
- AGAPLESION MARKUS KRANKENHAUS, Frankfurt am Main, Germany
| | | | - Jochen Klucken
- Centre Hospitalier de Luxembourg, Luxembourg Institute of Health, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Bjoern M Eskofier
- Machine Learning and Data Analytics Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Carl-Thiersch-Str. 2b, 91052, Erlangen, Germany
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19
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Fröhlich H, Bontridder N, Petrovska-Delacréta D, Glaab E, Kluge F, Yacoubi ME, Marín Valero M, Corvol JC, Eskofier B, Van Gyseghem JM, Lehericy S, Winkler J, Klucken J. Leveraging the Potential of Digital Technology for Better Individualized Treatment of Parkinson's Disease. Front Neurol 2022; 13:788427. [PMID: 35295840 PMCID: PMC8918525 DOI: 10.3389/fneur.2022.788427] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2021] [Accepted: 01/31/2022] [Indexed: 12/18/2022] Open
Abstract
Recent years have witnessed a strongly increasing interest in digital technology within medicine (sensor devices, specific smartphone apps) and specifically also neurology. Quantitative measures derived from digital technology could provide Digital Biomarkers (DMs) enabling a quantitative and continuous monitoring of disease symptoms, also outside clinics. This includes the possibility to continuously and sensitively monitor the response to treatment, hence opening the opportunity to adapt medication pathways quickly. In addition, DMs may in the future allow early diagnosis, stratification of patient subgroups and prediction of clinical outcomes. Thus, DMs could complement or in certain cases even replace classical examiner-based outcome measures and molecular biomarkers measured in cerebral spinal fluid, blood, urine, saliva, or other body liquids. Altogether, DMs could play a prominent role in the emerging field of precision medicine. However, realizing this vision requires dedicated research. First, advanced data analytical methods need to be developed and applied, which extract candidate DMs from raw signals. Second, these candidate DMs need to be validated by (a) showing their correlation to established clinical outcome measures, and (b) demonstrating their diagnostic and/or prognostic value compared to established biomarkers. These points again require the use of advanced data analytical methods, including machine learning. In addition, the arising ethical, legal and social questions associated with the collection and processing of sensitive patient data and the use of machine learning methods to analyze these data for better individualized treatment of the disease, must be considered thoroughly. Using Parkinson's Disease (PD) as a prime example of a complex multifactorial disorder, the purpose of this article is to critically review the current state of research regarding the use of DMs, discuss open challenges and highlight emerging new directions.
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Affiliation(s)
- Holger Fröhlich
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin, Germany
- Bonn-Aachen International Center for IT (b-it), University of Bonn, Bonn, Germany
| | - Noémi Bontridder
- Centre de Recherches Information, Droit et Societe, University of Namur, Namur, Belgium
| | | | - Enrico Glaab
- Luxembourg Center for Systems Medicine, University of Luxembourg, Esch, Luxembourg
| | - Felix Kluge
- Department of Artificial Intelligence in Biomedical Engineering, University of Erlangen Nuremberg, Erlangen, Germany
| | | | | | | | - Bjoern Eskofier
- Department of Artificial Intelligence in Biomedical Engineering, University of Erlangen Nuremberg, Erlangen, Germany
| | | | | | - Jürgen Winkler
- Department of Neurology, University Hospital Erlangen, Erlangen, Germany
| | - Jochen Klucken
- Luxembourg Center for Systems Medicine, University of Luxembourg, Esch, Luxembourg
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20
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Ullrich M, Roth N, Kuderle A, Richer R, Gladow T, Gasner H, Marxreiter F, Klucken J, Eskofier BM, Kluge F. Fall Risk Prediction in Parkinson's Disease Using Real-World Inertial Sensor Gait Data. IEEE J Biomed Health Inform 2022; 27:319-328. [PMID: 36260566 DOI: 10.1109/jbhi.2022.3215921] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Falls are an eminent risk for older adults and especially patients with neurodegenerative disorders, such as Parkinson's disease (PD). Recent advancements in wearable sensor technology and machine learning may provide a possibility for an individualized prediction of fall risk based on gait recordings from standardized gait tests or from unconstrained real-world scenarios. However, the most effective aggregation of continuous real-world data as well as the potential of unsupervised gait tests recorded over multiple days for fall risk prediction still need to be investigated. Therefore, we present a data set containing real-world gait and unsupervised 4x10-Meter-Walking-Tests of 40 PD patients, continuously recorded with foot-worn inertial sensors over a period of two weeks. In this prospective study, falls were self-reported during a three-month follow-up phase, serving as ground truth for fall risk prediction. The purpose of this study was to compare different data aggregation approaches and machine learning models for the prospective prediction of fall risk using gait parameters derived either from continuous real-world recordings or from unsupervised gait tests. The highest balanced accuracy of 74.0% (sensitivity: 60.0%, specificity: 88.0%) was achieved with a Random Forest Classifier applied to the real-world gait data when aggregating all walking bouts and days of each participant. Our findings suggest that fall risk can be predicted best by merging the entire two-week real-world gait data of a patient, outperforming the prediction using unsupervised gait tests (68.0% balanced accuracy) and contribute to an improved understanding of fall risk prediction.
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21
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Mallick MK, Biser S, Haridas A, Umesh V, Tönsing O, Yari IA, Ollenschläger M, Heckel M, Ostgathe C, Kluge F, Eskofier B, Steigleder T. Improving Dyspnoea Symptom Control of Patients in Palliative Care Using a Smart Patch-A Proof of Concept Study. Front Digit Health 2021; 3:765867. [PMID: 34913047 PMCID: PMC8666503 DOI: 10.3389/fdgth.2021.765867] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Accepted: 11/01/2021] [Indexed: 11/13/2022] Open
Abstract
The world of healthcare constantly aims to improve the lives of people while nurturing their health and comfort. Digital health and wearable technologies are aimed at making this possible. However, there are numerous factors that need to be addressed such as aging, disabilities, and health hazards. These factors are intensified in palliative care (PC) patients and limited hospital capacities make it challenging for health care providers (HCP) to handle the crisis. One of the most common symptoms reported by PC patients with severe conditions is dyspnoea. Monitoring devices with sufficient comfort could improve symptom control of patients with dyspnoea in PC. In this article, we discuss the proof-of-concept study to investigate a smart patch (SP), which monitors the pulmonary parameters: (a) breathing rate (BR) and inspiration to expiration ratio (I:E); markers for distress: (b) heart rate (HR) and heart rate variability (HRV), and (c) transmits real-time data securely to an adaptable user interface, primarily geared for palliative HCP but scalable to specific needs. The concept is verified by measuring and analyzing physiological signals from different electrode positions on the chest and comparing the results achieved with the gold standard Task Force Monitor (TFM).
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Affiliation(s)
- Mostafa Kamal Mallick
- Machine Learning and Data Analytics Lab, Department Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Sarah Biser
- Machine Learning and Data Analytics Lab, Department Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Aathira Haridas
- Machine Learning and Data Analytics Lab, Department Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Vaishnavi Umesh
- Machine Learning and Data Analytics Lab, Department Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Olaf Tönsing
- Machine Learning and Data Analytics Lab, Department Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Imrana Abdullahi Yari
- Machine Learning and Data Analytics Lab, Department Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Malte Ollenschläger
- Machine Learning and Data Analytics Lab, Department Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Maria Heckel
- Department of Palliative Medicine, Comprehensive Cancer Center Erlangen-EMN, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Christoph Ostgathe
- Department of Palliative Medicine, Comprehensive Cancer Center Erlangen-EMN, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Felix Kluge
- Machine Learning and Data Analytics Lab, Department Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Bjoern Eskofier
- Machine Learning and Data Analytics Lab, Department Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Tobias Steigleder
- Department of Palliative Medicine, Comprehensive Cancer Center Erlangen-EMN, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
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22
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Mazzà C, Alcock L, Aminian K, Becker C, Bertuletti S, Bonci T, Brown P, Brozgol M, Buckley E, Carsin AE, Caruso M, Caulfield B, Cereatti A, Chiari L, Chynkiamis N, Ciravegna F, Del Din S, Eskofier B, Evers J, Garcia Aymerich J, Gazit E, Hansen C, Hausdorff JM, Helbostad JL, Hiden H, Hume E, Paraschiv-Ionescu A, Ireson N, Keogh A, Kirk C, Kluge F, Koch S, Küderle A, Lanfranchi V, Maetzler W, Micó-Amigo ME, Mueller A, Neatrour I, Niessen M, Palmerini L, Pluimgraaff L, Reggi L, Salis F, Schwickert L, Scott K, Sharrack B, Sillen H, Singleton D, Soltani A, Taraldsen K, Ullrich M, Van Gelder L, Vereijken B, Vogiatzis I, Warmerdam E, Yarnall A, Rochester L. Technical validation of real-world monitoring of gait: a multicentric observational study. BMJ Open 2021; 11:e050785. [PMID: 34857567 PMCID: PMC8640671 DOI: 10.1136/bmjopen-2021-050785] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
INTRODUCTION Existing mobility endpoints based on functional performance, physical assessments and patient self-reporting are often affected by lack of sensitivity, limiting their utility in clinical practice. Wearable devices including inertial measurement units (IMUs) can overcome these limitations by quantifying digital mobility outcomes (DMOs) both during supervised structured assessments and in real-world conditions. The validity of IMU-based methods in the real-world, however, is still limited in patient populations. Rigorous validation procedures should cover the device metrological verification, the validation of the algorithms for the DMOs computation specifically for the population of interest and in daily life situations, and the users' perspective on the device. METHODS AND ANALYSIS This protocol was designed to establish the technical validity and patient acceptability of the approach used to quantify digital mobility in the real world by Mobilise-D, a consortium funded by the European Union (EU) as part of the Innovative Medicine Initiative, aiming at fostering regulatory approval and clinical adoption of DMOs.After defining the procedures for the metrological verification of an IMU-based device, the experimental procedures for the validation of algorithms used to calculate the DMOs are presented. These include laboratory and real-world assessment in 120 participants from five groups: healthy older adults; chronic obstructive pulmonary disease, Parkinson's disease, multiple sclerosis, proximal femoral fracture and congestive heart failure. DMOs extracted from the monitoring device will be compared with those from different reference systems, chosen according to the contexts of observation. Questionnaires and interviews will evaluate the users' perspective on the deployed technology and relevance of the mobility assessment. ETHICS AND DISSEMINATION The study has been granted ethics approval by the centre's committees (London-Bloomsbury Research Ethics committee; Helsinki Committee, Tel Aviv Sourasky Medical Centre; Medical Faculties of The University of Tübingen and of the University of Kiel). Data and algorithms will be made publicly available. TRIAL REGISTRATION NUMBER ISRCTN (12246987).
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Affiliation(s)
- Claudia Mazzà
- INSIGNEO Institute for in silico Medicine, The University of Sheffield, Sheffield, UK
- Department of Mechanical Engineering, The University of Sheffield, Sheffield, UK
| | - Lisa Alcock
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - Kamiar Aminian
- Laboratory of Movement Analysis and Measurement, Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland
| | - Clemens Becker
- Robert Bosch Gesellschaft für Medizinische Forschung, Stuttgart, Germany
| | - Stefano Bertuletti
- Department of Biomedical Sciences, University of Sassari, Sassari, Sardegna, Italy
| | - Tecla Bonci
- INSIGNEO Institute for in silico Medicine, The University of Sheffield, Sheffield, UK
- Department of Mechanical Engineering, The University of Sheffield, Sheffield, UK
| | - Philip Brown
- The Newcastle Upon Tyne Hospitals NHS Foundation Trust, Newcastle Upon Tyne, UK
| | - Marina Brozgol
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Ellen Buckley
- INSIGNEO Institute for in silico Medicine, The University of Sheffield, Sheffield, UK
- Department of Mechanical Engineering, The University of Sheffield, Sheffield, UK
| | - Anne-Elie Carsin
- ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
- IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
| | - Marco Caruso
- Dipartimento di Elettronica e Telecomunicazioni, Politecnico di Torino, Torino, Italy
- PolitoBIOMed Lab - Biomedical Engineering Lab, Politecnico di Torino, Torino, Italy
| | - Brian Caulfield
- Insight Centre for Data Analytics, O'Brien Science Centre, University College Dublin, Dublin, Ireland
- UCD School of Public Health, Physiotherapy and Sports Science, University College Dublin, Dublin, Ireland
| | - Andrea Cereatti
- Dipartimento di Elettronica e Telecomunicazioni, Politecnico di Torino, Torino, Italy
| | - Lorenzo Chiari
- Department of Electrical, Electronic and Information Engineering «Guglielmo Marconi», University of Bologna, Bologna, Italy
- Health Sciences and Technologies-Interdepartmental Center for Industrial Research (CIRI-SDV), University of Bologna, Bologna, Italy
| | - Nikolaos Chynkiamis
- Department of Sport, Exercise and Rehabilitation, Northumbria University Newcastle, Newcastle upon Tyne, UK
| | - Fabio Ciravegna
- INSIGNEO Institute for in silico Medicine, The University of Sheffield, Sheffield, UK
- Department of Computer Science, The University of Sheffield, Sheffield, UK
| | - Silvia Del Din
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - Björn Eskofier
- Machine Learning and Data Analytics Lab, Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Jordi Evers
- McRoberts BV, Den Haag, Zuid-Holland, Netherlands
| | - Judith Garcia Aymerich
- ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Eran Gazit
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Clint Hansen
- Department of Neurology, University Medical Center Schleswig-Holstein Campus Kiel, Kiel, Germany
| | - Jeffrey M Hausdorff
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
- Department of Physical Therapy, Sackler Faculty of Medicine & Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Jorunn L Helbostad
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, Trondheim, Norway
| | - Hugo Hiden
- School of Computing, Newcastle University, Newcastle upon Tyne, UK
| | - Emily Hume
- Department of Sport, Exercise and Rehabilitation, Northumbria University Newcastle, Newcastle upon Tyne, UK
| | - Anisoara Paraschiv-Ionescu
- Laboratory of Movement Analysis and Measurement, Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland
| | - Neil Ireson
- INSIGNEO Institute for in silico Medicine, The University of Sheffield, Sheffield, UK
- Department of Computer Science, The University of Sheffield, Sheffield, UK
| | - Alison Keogh
- Insight Centre for Data Analytics, O'Brien Science Centre, University College Dublin, Dublin, Ireland
- UCD School of Public Health, Physiotherapy and Sports Science, University College Dublin, Dublin, Ireland
| | - Cameron Kirk
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - Felix Kluge
- Machine Learning and Data Analytics Lab, Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Sarah Koch
- ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Arne Küderle
- Machine Learning and Data Analytics Lab, Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Vitaveska Lanfranchi
- INSIGNEO Institute for in silico Medicine, The University of Sheffield, Sheffield, UK
- Department of Computer Science, The University of Sheffield, Sheffield, UK
| | - Walter Maetzler
- Department of Neurology, University Medical Center Schleswig-Holstein Campus Kiel, Kiel, Germany
| | - M Encarna Micó-Amigo
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - Arne Mueller
- Novartis Institutes of Biomedical Research, Novartis Pharma AG, Basel, Switzerland
| | - Isabel Neatrour
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | | | - Luca Palmerini
- Department of Electrical, Electronic and Information Engineering «Guglielmo Marconi», University of Bologna, Bologna, Italy
- Health Sciences and Technologies-Interdepartmental Center for Industrial Research (CIRI-SDV), University of Bologna, Bologna, Italy
| | | | - Luca Reggi
- Health Sciences and Technologies-Interdepartmental Center for Industrial Research (CIRI-SDV), University of Bologna, Bologna, Italy
| | - Francesca Salis
- Department of Biomedical Sciences, University of Sassari, Sassari, Sardegna, Italy
| | - Lars Schwickert
- Robert Bosch Gesellschaft für Medizinische Forschung, Stuttgart, Germany
| | - Kirsty Scott
- INSIGNEO Institute for in silico Medicine, The University of Sheffield, Sheffield, UK
- Department of Mechanical Engineering, The University of Sheffield, Sheffield, UK
| | - Basil Sharrack
- Department of Neuroscience and Sheffield NIHR Translational Neuroscience BRC, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK
| | - Henrik Sillen
- Digital Health R&D, AstraZeneca Sweden, Sodertalje, Sweden
| | - David Singleton
- Insight Centre for Data Analytics, O'Brien Science Centre, University College Dublin, Dublin, Ireland
- UCD School of Public Health, Physiotherapy and Sports Science, University College Dublin, Dublin, Ireland
| | - Abolfazi Soltani
- Laboratory of Movement Analysis and Measurement, Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland
| | - Kristin Taraldsen
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, Trondheim, Norway
| | - Martin Ullrich
- Machine Learning and Data Analytics Lab, Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Linda Van Gelder
- INSIGNEO Institute for in silico Medicine, The University of Sheffield, Sheffield, UK
- Department of Mechanical Engineering, The University of Sheffield, Sheffield, UK
| | - Beatrix Vereijken
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, Trondheim, Norway
| | - Ioannis Vogiatzis
- Department of Sport, Exercise and Rehabilitation, Northumbria University Newcastle, Newcastle upon Tyne, UK
| | - Elke Warmerdam
- Department of Neurology, University Medical Center Schleswig-Holstein Campus Kiel, Kiel, Germany
| | - Alison Yarnall
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
- The Newcastle Upon Tyne Hospitals NHS Foundation Trust, Newcastle Upon Tyne, UK
| | - Lynn Rochester
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
- The Newcastle Upon Tyne Hospitals NHS Foundation Trust, Newcastle Upon Tyne, UK
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Abdullahi Yari I, Dehling T, Kluge F, Geck J, Sunyaev A, Eskofier B. Security Engineering of Patient-Centered Health Care Information Systems in Peer-to-Peer Environments: Systematic Review. J Med Internet Res 2021; 23:e24460. [PMID: 34779788 PMCID: PMC8663665 DOI: 10.2196/24460] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2020] [Revised: 05/20/2021] [Accepted: 08/02/2021] [Indexed: 11/13/2022] Open
Abstract
Background Patient-centered health care information systems (PHSs) enable patients to take control and become knowledgeable about their own health, preferably in a secure environment. Current and emerging PHSs use either a centralized database, peer-to-peer (P2P) technology, or distributed ledger technology for PHS deployment. The evolving COVID-19 decentralized Bluetooth-based tracing systems are examples of disease-centric P2P PHSs. Although using P2P technology for the provision of PHSs can be flexible, scalable, resilient to a single point of failure, and inexpensive for patients, the use of health information on P2P networks poses major security issues as users must manage information security largely by themselves. Objective This study aims to identify the inherent security issues for PHS deployment in P2P networks and how they can be overcome. In addition, this study reviews different P2P architectures and proposes a suitable architecture for P2P PHS deployment. Methods A systematic literature review was conducted following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) reporting guidelines. Thematic analysis was used for data analysis. We searched the following databases: IEEE Digital Library, PubMed, Science Direct, ACM Digital Library, Scopus, and Semantic Scholar. The search was conducted on articles published between 2008 and 2020. The Common Vulnerability Scoring System was used as a guide for rating security issues. Results Our findings are consolidated into 8 key security issues associated with PHS implementation and deployment on P2P networks and 7 factors promoting them. Moreover, we propose a suitable architecture for P2P PHSs and guidelines for the provision of PHSs while maintaining information security. Conclusions Despite the clear advantages of P2P PHSs, the absence of centralized controls and inconsistent views of the network on some P2P systems have profound adverse impacts in terms of security. The security issues identified in this study need to be addressed to increase patients’ intention to use PHSs on P2P networks by making them safe to use.
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Affiliation(s)
- Imrana Abdullahi Yari
- Department of Artificial Intelligence in Biomedical Engineering, Machine Learning and Data Analytics Lab, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany
| | - Tobias Dehling
- Institute of Applied Informatics and Formal Description Methods, Karlsruhe Institute of Technology, Karlsruhe, Germany.,KASTEL Security Research Labs, Karlsruhe, Germany
| | - Felix Kluge
- Department of Artificial Intelligence in Biomedical Engineering, Machine Learning and Data Analytics Lab, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany
| | | | - Ali Sunyaev
- Institute of Applied Informatics and Formal Description Methods, Karlsruhe Institute of Technology, Karlsruhe, Germany.,KASTEL Security Research Labs, Karlsruhe, Germany
| | - Bjoern Eskofier
- Department of Artificial Intelligence in Biomedical Engineering, Machine Learning and Data Analytics Lab, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany
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24
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Roth N, Wieland GP, Kuderle A, Ullrich M, Gladow T, Marxreiter F, Klucken J, Eskofier BM, Kluge F. Do We Walk Differently at Home? A Context-Aware Gait Analysis System in Continuous Real-World Environments. Annu Int Conf IEEE Eng Med Biol Soc 2021; 2021:1932-1935. [PMID: 34891665 DOI: 10.1109/embc46164.2021.9630378] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Driven by the advancements of wearable sensors and signal processing algorithms, studies on continuous real-world monitoring are of major interest in the field of clinical gait and motion analysis. While real-world studies enable a more detailed and realistic insight into various mobility parameters such as walking speed, confounding and environmental factors might skew those digital mobility outcomes (DMOs), making the interpretation of results challenging. To consider confounding factors, context information needs to be included in the analysis. In this work, we present a context-aware mobile gait analysis system that can distinguish between gait recorded at home and not at home based on Bluetooth proximity information. The system was evaluated on 9 healthy subjects and 6 Parkinsons disease (PD) patients. The classification of the at home/not at home context reached an average F1-score of 98.2 ± 3.2 %. A context-aware analysis of gait parameters revealed different walking bout length distributions between the two environmental conditions. Furthermore, a reduction of gait speed within the at home context compared to walking not at home of 8.9 ± 9.4 % and 8.7 ±5.9 % on average for healthy and PD subjects was found, respectively. Our results indicate the influence of the recording environment on DMOs and, therefore, emphasize the importance of context in the analysis of continuous motion data. Hence, the presented work contributes to a better understanding of confounding factors for future real-world studies.
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25
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Happold J, Richer R, Kuderle A, Gabner H, Klucken J, Eskofier BM, Kluge F. Evaluation of Orthostatic Reactions in Real-World Environments Using Wearable Sensors. Annu Int Conf IEEE Eng Med Biol Soc 2021; 2021:6987-6990. [PMID: 34892711 DOI: 10.1109/embc46164.2021.9630842] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
As global life expectancy is constantly rising, the early detection of age-related, neurodegenerative diseases, such as Parkinson's disease, is becoming increasingly important. Patients suffering from Parkinson's disease often show autonomic nervous system dysfunction which is why its examination is an important diagnostic tool. Measuring the response of the heart rate (variability) to postural transitions and thereby assessing the orthostatic reaction is a common indicator of autonomic nervous system functioning. However, since these measurements are commonly performed in a clinical environment, results can be impaired by the white coat effect. To reduce this influence as well as inter- and intra-day variations, our work aims to investigate the assessment of orthostatic reactions in free-living environments. We collected IMU and ECG data of seven healthy participants over four days and evaluated differences in orthostatic reactions between standardized tests at lab, at home, as well as unsupervised recordings during real-world conditions. Except for the first lab recording, we detected significant changes in heart rate due to postural transitions in all recording settings, with the strongest response occurring during standardized tests at home. Our findings show that real-world assessment of orthostatic reactions is possible and provides comparable results to supervised assessments in lab settings. Additionally, our results indicate high inter- and intra-day variability which motivates the continuous orthostatic reaction measurement over the span of multiple days. We are convinced that our presented approach provides a first step towards unobtrusive assessment of orthostatic reactions in real-world environments, which might enable a more reliable early detection of disorders of the autonomic nervous system.
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Ullrich M, Kuderle A, Reggi L, Cereatti A, Eskofier BM, Kluge F. Machine learning-based distinction of left and right foot contacts in lower back inertial sensor gait data. Annu Int Conf IEEE Eng Med Biol Soc 2021; 2021:5958-5961. [PMID: 34892475 DOI: 10.1109/embc46164.2021.9630653] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Digital gait measures derived from wearable inertial sensors have been shown to support the treatment of patients with motor impairments. From a technical perspective, the detection of left and right initial foot contacts (ICs) is essential for the computation of stride-by-stride outcome measures including gait asymmetry. However, in a majority of studies only one sensor close to the center of mass is used, complicating the assignment of detected ICs to the respective foot. Therefore, we developed an algorithm including supervised machine learning (ML) models for the robust classification of left and right ICs using multiple features from the gyroscope located at the lower back. The approach was tested on a data set including 40 participants (ten healthy controls, ten hemiparetic, ten Parkinson's disease, and ten Huntington's disease patients) and reached an accuracy of 96.3% for the overall data set and up to 100.0% for the Parkinson's sub data set. These results were compared to a state-of-the-art algorithm. The ML approaches outperformed this traditional algorithm in all subgroups. Our study contributes to an improved classification of left and right ICs in inertial sensor signals recorded at the lower back and thus enables a reliable computation of clinically relevant mobility measures.
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27
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Polhemus A, Ortiz LD, Brittain G, Chynkiamis N, Salis F, Gaßner H, Gross M, Kirk C, Rossanigo R, Taraldsen K, Balta D, Breuls S, Buttery S, Cardenas G, Endress C, Gugenhan J, Keogh A, Kluge F, Koch S, Micó-Amigo ME, Nerz C, Sieber C, Williams P, Bergquist R, Bosch de Basea M, Buckley E, Hansen C, Mikolaizak AS, Schwickert L, Scott K, Stallforth S, van Uem J, Vereijken B, Cereatti A, Demeyer H, Hopkinson N, Maetzler W, Troosters T, Vogiatzis I, Yarnall A, Becker C, Garcia-Aymerich J, Leocani L, Mazzà C, Rochester L, Sharrack B, Frei A, Puhan M. Walking on common ground: a cross-disciplinary scoping review on the clinical utility of digital mobility outcomes. NPJ Digit Med 2021; 4:149. [PMID: 34650191 PMCID: PMC8516969 DOI: 10.1038/s41746-021-00513-5] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Accepted: 08/09/2021] [Indexed: 02/08/2023] Open
Abstract
Physical mobility is essential to health, and patients often rate it as a high-priority clinical outcome. Digital mobility outcomes (DMOs), such as real-world gait speed or step count, show promise as clinical measures in many medical conditions. However, current research is nascent and fragmented by discipline. This scoping review maps existing evidence on the clinical utility of DMOs, identifying commonalities across traditional disciplinary divides. In November 2019, 11 databases were searched for records investigating the validity and responsiveness of 34 DMOs in four diverse medical conditions (Parkinson's disease, multiple sclerosis, chronic obstructive pulmonary disease, hip fracture). Searches yielded 19,672 unique records. After screening, 855 records representing 775 studies were included and charted in systematic maps. Studies frequently investigated gait speed (70.4% of studies), step length (30.7%), cadence (21.4%), and daily step count (20.7%). They studied differences between healthy and pathological gait (36.4%), associations between DMOs and clinical measures (48.8%) or outcomes (4.3%), and responsiveness to interventions (26.8%). Gait speed, step length, cadence, step time and step count exhibited consistent evidence of validity and responsiveness in multiple conditions, although the evidence was inconsistent or lacking for other DMOs. If DMOs are to be adopted as mainstream tools, further work is needed to establish their predictive validity, responsiveness, and ecological validity. Cross-disciplinary efforts to align methodology and validate DMOs may facilitate their adoption into clinical practice.
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Affiliation(s)
- Ashley Polhemus
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland.
| | - Laura Delgado Ortiz
- ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
- CIBER Epidemiología y Salud Pública, Barcelona, Spain
| | - Gavin Brittain
- Department of Neuroscience and Sheffield NIHR Translational Neuroscience BRC, Sheffield Teaching Hospitals NHS Foundation Trust & University of Sheffield, Sheffield, England
| | - Nikolaos Chynkiamis
- Department of Sport, Exercise and Rehabilitation, Faculty of Health and Life Sciences, Northumbria University Newcastle, Newcastle, UK
| | - Francesca Salis
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
| | - Heiko Gaßner
- Department of Molecular Neurology, University Hospital Erlangen, Erlangen, Germany
| | - Michaela Gross
- Department of Clinical Gerontology, Robert-Bosch-Hospital, Stuttgart, Germany
| | - Cameron Kirk
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - Rachele Rossanigo
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
| | - Kristin Taraldsen
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, Trondheim, Norway
| | - Diletta Balta
- Department of Electronics and Telecommunications, Politecnico di Torino, Torino, Italy
| | - Sofie Breuls
- Department of Rehabilitation Sciences, KU Leuven, Leuven, Belgium
- Department of Respiratory Diseases, University hospitals Leuven, Leuven, Belgium
| | - Sara Buttery
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Gabriela Cardenas
- ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
- CIBER Epidemiología y Salud Pública, Barcelona, Spain
| | - Christoph Endress
- Department of Clinical Gerontology, Robert-Bosch-Hospital, Stuttgart, Germany
| | - Julia Gugenhan
- Department of Clinical Gerontology, Robert-Bosch-Hospital, Stuttgart, Germany
| | - Alison Keogh
- Insight Centre for Data Analytics, University College Dublin, Dublin, Ireland
| | - Felix Kluge
- Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Sarah Koch
- ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
- CIBER Epidemiología y Salud Pública, Barcelona, Spain
| | - M Encarna Micó-Amigo
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - Corinna Nerz
- Department of Clinical Gerontology, Robert-Bosch-Hospital, Stuttgart, Germany
| | - Chloé Sieber
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland
| | - Parris Williams
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Ronny Bergquist
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, Trondheim, Norway
| | - Magda Bosch de Basea
- ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
- CIBER Epidemiología y Salud Pública, Barcelona, Spain
| | - Ellen Buckley
- Insigneo Institute, Department of Mechanical Engineering, University of Sheffield, Sheffield, UK
| | - Clint Hansen
- Department of Neurology, University Medical Center Schleswig-Holstein, Kiel, Germany
| | | | - Lars Schwickert
- Department of Clinical Gerontology, Robert-Bosch-Hospital, Stuttgart, Germany
| | - Kirsty Scott
- Insigneo Institute, Department of Mechanical Engineering, University of Sheffield, Sheffield, UK
| | - Sabine Stallforth
- Department of Molecular Neurology, University Hospital Erlangen, Erlangen, Germany
| | - Janet van Uem
- Department of Neurology, University Medical Center Schleswig-Holstein, Kiel, Germany
| | - Beatrix Vereijken
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, Trondheim, Norway
| | - Andrea Cereatti
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
- Department of Electronics and Telecommunications, Politecnico di Torino, Torino, Italy
| | - Heleen Demeyer
- Department of Rehabilitation Sciences, KU Leuven, Leuven, Belgium
- Department of Respiratory Diseases, University hospitals Leuven, Leuven, Belgium
- Department of Rehabilitation Sciences, Ghent University, Ghent, Belgium
| | | | - Walter Maetzler
- Department of Neurology, University Medical Center Schleswig-Holstein, Kiel, Germany
| | - Thierry Troosters
- Department of Rehabilitation Sciences, KU Leuven, Leuven, Belgium
- Department of Respiratory Diseases, University hospitals Leuven, Leuven, Belgium
| | - Ioannis Vogiatzis
- Department of Sport, Exercise and Rehabilitation, Faculty of Health and Life Sciences, Northumbria University Newcastle, Newcastle, UK
| | - Alison Yarnall
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - Clemens Becker
- Department of Clinical Gerontology, Robert-Bosch-Hospital, Stuttgart, Germany
| | - Judith Garcia-Aymerich
- ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
- CIBER Epidemiología y Salud Pública, Barcelona, Spain
| | - Letizia Leocani
- Department of Neurology, San Raffaele University, Milan, Italy
| | - Claudia Mazzà
- Insigneo Institute, Department of Mechanical Engineering, University of Sheffield, Sheffield, UK
| | - Lynn Rochester
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - Basil Sharrack
- Department of Neuroscience and Sheffield NIHR Translational Neuroscience BRC, Sheffield Teaching Hospitals NHS Foundation Trust & University of Sheffield, Sheffield, England
| | - Anja Frei
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland
| | - Milo Puhan
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland
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Ullrich M, Mucke A, Kuderle A, Roth N, Gladow T, Gabner H, Marxreiter F, Klucken J, Eskofier BM, Kluge F. Detection of Unsupervised Standardized Gait Tests From Real-World Inertial Sensor Data in Parkinson's Disease. IEEE Trans Neural Syst Rehabil Eng 2021; 29:2103-2111. [PMID: 34633932 DOI: 10.1109/tnsre.2021.3119390] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Gait tests as part of home monitoring study protocols for patients with movement disorders may provide valuable standardized anchor-points for real-world gait analysis using inertial measurement units (IMUs). However, analyzing unsupervised gait tests relies on reliable test annotations by the patients requiring a potentially error-prone interaction with the recording system. To overcome this limitation, this work presents a novel algorithmic pipeline for the automated detection of unsupervised standardized gait tests from continuous real-world IMU data. In a study with twelve Parkinson's disease patients, we recorded real-world gait data over two weeks using foot-worn IMUs. During continuous daily recordings, the participants performed series of three consecutive 4×10 -Meters-Walking-Tests ( 4×10 MWTs) at different walking speeds, besides their usual daily-living activities. The algorithm first detected these gait test series using a gait sequence detection algorithm, a peak enhancement pipeline, and subsequence Dynamic Time Warping and then decomposed them into single 4×10 MWTs based on the walking speed. In the evaluation with 419 available gait test series, the detection reached an F1-score of 88.9% and the decomposition an F1-score of 94.0%. A concurrent validity evaluation revealed very good agreement between spatio-temporal gait parameters derived from manually labelled and automatically detected 4×10 MWTs. Our algorithm allows to remove the burden of system interaction from the patients and reduces the time for manual data annotation for researchers. The study contributes to an improved automated processing of real-world IMU gait data and enables a simple integration of standardized tests into continuous long-term recordings. This will help to bridge the gap between supervised and unsupervised gait assessment.
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29
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Kluge F, Del Din S, Cereatti A, Gaßner H, Hansen C, Helbostad JL, Klucken J, Küderle A, Müller A, Rochester L, Ullrich M, Eskofier BM, Mazzà C. Consensus based framework for digital mobility monitoring. PLoS One 2021; 16:e0256541. [PMID: 34415959 PMCID: PMC8378707 DOI: 10.1371/journal.pone.0256541] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Accepted: 08/09/2021] [Indexed: 12/31/2022] Open
Abstract
Digital mobility assessment using wearable sensor systems has the potential to capture walking performance in a patient's natural environment. It enables monitoring of health status and disease progression and evaluation of interventions in real-world situations. In contrast to laboratory settings, real-world walking occurs in non-conventional environments and under unconstrained and uncontrolled conditions. Despite the general understanding, there is a lack of agreed definitions about what constitutes real-world walking, impeding the comparison and interpretation of the acquired data across systems and studies. The goal of this study was to obtain expert-based consensus on specific aspects of real-world walking and to provide respective definitions in a common terminological framework. An adapted Delphi method was used to obtain agreed definitions related to real-world walking. In an online survey, 162 participants from a panel of academic, clinical and industrial experts with experience in the field of gait analysis were asked for agreement on previously specified definitions. Descriptive statistics was used to evaluate whether consent (> 75% agreement as defined a priori) was reached. Of 162 experts invited to participate, 51 completed all rounds (31.5% response rate). We obtained consensus on all definitions ("Walking" > 90%, "Purposeful" > 75%, "Real-world" > 90%, "Walking bout" > 80%, "Walking speed" > 75%, "Turning" > 90% agreement) after two rounds. The identification of a consented set of real-world walking definitions has important implications for the development of assessment and analysis protocols, as well as for the reporting and comparison of digital mobility outcomes across studies and systems. The definitions will serve as a common framework for implementing digital and mobile technologies for gait assessment and are an important link for the transition from supervised to unsupervised gait assessment.
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Affiliation(s)
- Felix Kluge
- Machine Learning and Data Analytics Lab, Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany
| | - Silvia Del Din
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Andrea Cereatti
- Department of Electronics and Telecommunications, Politecnico di Torino, Torino, Italy
| | - Heiko Gaßner
- Department of Molecular Neurology, University Hospital Erlangen, Erlangen, Germany
| | - Clint Hansen
- Department of Neurology, University of Kiel, Kiel, Germany
| | - Jorunn L. Helbostad
- Department of Neuromedicine and Movement Science, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
| | - Jochen Klucken
- Department of Molecular Neurology, University Hospital Erlangen, Erlangen, Germany
| | - Arne Küderle
- Machine Learning and Data Analytics Lab, Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany
| | | | - Lynn Rochester
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
- The Newcastle upon Tyne NHS Foundation Trust, Newcastle upon Tyne, United Kingdom
| | - Martin Ullrich
- Machine Learning and Data Analytics Lab, Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany
| | - Bjoern M. Eskofier
- Machine Learning and Data Analytics Lab, Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany
| | - Claudia Mazzà
- Department of Mechanical Engineering & Insigneo Institute for in Silico Medicine, The University of Sheffield, Sheffield, United Kingdom
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Ibrahim AA, Küderle A, Gaßner H, Klucken J, Eskofier BM, Kluge F. Inertial sensor-based gait parameters reflect patient-reported fatigue in multiple sclerosis. J Neuroeng Rehabil 2020; 17:165. [PMID: 33339530 PMCID: PMC7749504 DOI: 10.1186/s12984-020-00798-9] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2020] [Accepted: 12/09/2020] [Indexed: 12/30/2022] Open
Abstract
Background Multiple sclerosis (MS) is a disabling disease affecting the central nervous system and consequently the whole body’s functional systems resulting in different gait disorders. Fatigue is the most common symptom in MS with a prevalence of 80%. Previous research studied the relation between fatigue and gait impairment using stationary gait analysis systems and short gait tests (e.g. timed 25 ft walk). However, wearable inertial sensors providing gait data from longer and continuous gait bouts have not been used to assess the relation between fatigue and gait parameters in MS. Therefore, the aim of this study was to evaluate the association between fatigue and spatio-temporal gait parameters extracted from wearable foot-worn sensors and to predict the degree of fatigue. Methods Forty-nine patients with MS (32 women; 17 men; aged 41.6 years, EDSS 1.0–6.5) were included where each participant was equipped with a small Inertial Measurement Unit (IMU) on each foot. Spatio-temporal gait parameters were obtained from the 6-min walking test, and the Borg scale of perceived exertion was used to represent fatigue. Gait parameters were normalized by taking the difference of averaged gait parameters between the beginning and end of the test to eliminate inter-individual differences. Afterwards, normalized parameters were transformed to principle components that were used as input to a Random Forest regression model to formulate the relationship between gait parameters and fatigue. Results Six principal components were used as input to our model explaining more than 90% of variance within our dataset. Random Forest regression was used to predict fatigue. The model was validated using 10-fold cross validation and the mean absolute error was 1.38 points. Principal components consisting mainly of stride time, maximum toe clearance, heel strike angle, and stride length had large contributions (67%) to the predictions made by the Random Forest. Conclusions The level of fatigue can be predicted based on spatio-temporal gait parameters obtained from an IMU based system. The results can help therapists to monitor fatigue before and after treatment and in rehabilitation programs to evaluate their efficacy. Furthermore, this can be used in home monitoring scenarios where therapists can monitor fatigue using IMUs reducing time and effort of patients and therapists.
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Affiliation(s)
- Alzhraa A Ibrahim
- Machine Learning and Data Analytics Lab, Friedrich-Alexander-University Erlangen-Nürnberg (FAU), Erlangen, Germany. .,Computer Science Department, Faculty of Computers and Information, Assiut University, Asyut, Egypt.
| | - Arne Küderle
- Machine Learning and Data Analytics Lab, Friedrich-Alexander-University Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Heiko Gaßner
- Department of Molecular Neurology, University Hospital Erlangen, Erlangen, Bavaria, Germany
| | - Jochen Klucken
- Department of Molecular Neurology, University Hospital Erlangen, Erlangen, Bavaria, Germany.,Fraunhofer Institut for Integrated Circuits, Erlangen, Bavaria, Germany.,Medical Valley Digital Health Application Center, Bamberg, Bavaria, Germany
| | - Bjoern M Eskofier
- Machine Learning and Data Analytics Lab, Friedrich-Alexander-University Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Felix Kluge
- Machine Learning and Data Analytics Lab, Friedrich-Alexander-University Erlangen-Nürnberg (FAU), Erlangen, Germany
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Tietsch M, Muaremi A, Clay I, Kluge F, Hoefling H, Ullrich M, Küderle A, Eskofier BM, Müller A. Robust Step Detection from Different Waist-Worn Sensor Positions: Implications for Clinical Studies. Digit Biomark 2020; 4:50-58. [PMID: 33442580 PMCID: PMC7768099 DOI: 10.1159/000511611] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Accepted: 09/15/2020] [Indexed: 11/19/2022] Open
Abstract
Analyzing human gait with inertial sensors provides valuable insights into a wide range of health impairments, including many musculoskeletal and neurological diseases. A representative and reliable assessment of gait requires continuous monitoring over long periods and ideally takes place in the subjects' habitual environment (real-world). An inconsistent sensor wearing position can affect gait characterization and influence clinical study results, thus clinical study protocols are typically highly proscriptive, instructing all participants to wear the sensor in a uniform manner. This restrictive approach improves data quality but reduces overall adherence. In this work, we analyze the impact of altering the sensor wearing position around the waist on sensor signal and step detection. We demonstrate that an asymmetrically worn sensor leads to additional odd-harmonic frequency components in the frequency spectrum. We propose a robust solution for step detection based on autocorrelation to overcome sensor position variation (sensitivity = 0.99, precision = 0.99). The proposed solution reduces the impact of inconsistent sensor positioning on gait characterization in clinical studies, thus providing more flexibility to protocol implementation and more freedom to participants to wear the sensor in the position most comfortable to them. This work is a first step towards truly position-agnostic gait assessment in clinical settings.
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Affiliation(s)
- Matthias Tietsch
- Novartis Institutes of Biomedical Research, Novartis Pharma AG, Basel, Switzerland
- Machine Learning and Data Analytics Lab, Department of Computer Science, Friedrich-Alexander University Erlangen-Nürnberg, Nürnberg, Germany
| | - Amir Muaremi
- Novartis Institutes of Biomedical Research, Novartis Pharma AG, Basel, Switzerland
| | - Ieuan Clay
- Evidation Health Inc., San Mateo, California, USA
| | - Felix Kluge
- Machine Learning and Data Analytics Lab, Department of Computer Science, Friedrich-Alexander University Erlangen-Nürnberg, Nürnberg, Germany
| | - Holger Hoefling
- Novartis Institutes of Biomedical Research, Novartis Pharma AG, Basel, Switzerland
| | - Martin Ullrich
- Machine Learning and Data Analytics Lab, Department of Computer Science, Friedrich-Alexander University Erlangen-Nürnberg, Nürnberg, Germany
| | - Arne Küderle
- Machine Learning and Data Analytics Lab, Department of Computer Science, Friedrich-Alexander University Erlangen-Nürnberg, Nürnberg, Germany
| | - Bjoern M. Eskofier
- Machine Learning and Data Analytics Lab, Department of Computer Science, Friedrich-Alexander University Erlangen-Nürnberg, Nürnberg, Germany
| | - Arne Müller
- Novartis Institutes of Biomedical Research, Novartis Pharma AG, Basel, Switzerland
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Fischer S, Ullrich M, Kuderle A, Gasner H, Klucken J, Eskofier BM, Kluge F. Automatic clinical gait test detection from inertial sensor data. Annu Int Conf IEEE Eng Med Biol Soc 2020; 2020:789-792. [PMID: 33018104 DOI: 10.1109/embc44109.2020.9176440] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
The analysis of gait data is one approach to support clinicians with the diagnosis and therapy of diseases, for example Parkinson's disease (PD). Traditionally, gait data of standardized tests in the clinic is analyzed, ensuring a predefined setting. In recent years, long-term home-based gait analysis has been used to acquire a more representative picture of the patient's disease status. Data is recorded in a less artificial setting and therefore allows a more realistic perception of the disease progression. However, fully unsupervised gait data without additional context information impedes interpretation. As an intermediate solution, performance of gait tests at home was introduced. Integration of instrumented gait test requires annotations of those tests for their identification and further processing. To overcome these limitations, we developed an algorithm for automatic detection of standardized gait tests from continuous sensor data with the goal of making manual annotations obsolete. The method is based on dynamic time warping, which compares an input signal with a predefined template and quantifies similarity between both. Different templates were compared and an optimized template was created. The classification scored a F1-measure of 86.7% for evaluation on a data set acquired in a clinical setting. We believe that this approach can be transferred to home-monitoring systems and will facilitate a more efficient and automated gait analysis.
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Ceron JD, Kluge F, Küderle A, Eskofier BM, López DM. Simultaneous Indoor Pedestrian Localization and House Mapping Based on Inertial Measurement Unit and Bluetooth Low-Energy Beacon Data. Sensors (Basel) 2020; 20:E4742. [PMID: 32842566 PMCID: PMC7506668 DOI: 10.3390/s20174742] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/21/2020] [Revised: 07/27/2020] [Accepted: 08/03/2020] [Indexed: 12/12/2022]
Abstract
Indoor location estimation is crucial to provide context-based assistance in home environments. In this study, a method for simultaneous indoor pedestrian localization and house mapping is proposed and evaluated. The method fuses a person's movement data from an Inertial Measurement Unit (IMU) with proximity and activity-related data from Bluetooth Low-Energy (BLE) beacons deployed in the indoor environment. The person's and beacons' localization is performed simultaneously using a combination of particle and Kalman Filters. We evaluated the method using data from eight participants who performed different activities in an indoor environment. As a result, the average participant's localization error was 1.05 ± 0.44 m, and the average beacons' localization error was 0.82 ± 0.24 m. The proposed method is able to construct a map of the indoor environment by localizing the BLE beacons and simultaneously locating the person. The results obtained demonstrate that the proposed method could point to a promising roadmap towards the development of simultaneous localization and home mapping system based only on one IMU and a few BLE beacons. To the best of our knowledge, this is the first method that includes the beacons' data movement as activity-related events in a method for pedestrian Simultaneous Localization and Mapping (SLAM).
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Affiliation(s)
- Jesus D. Ceron
- Telematics Engineering Research Group, Telematics Department, Universidad Del Cauca (Unicauca), Popayán 190002, Colombia;
- Machine Learning and Data Analytics Lab, Computer Science Department, Friedrich-Alexander University, Erlangen-Nürnberg (FAU), 91052 Erlangen, Germany; (F.K.); (A.K.)
| | - Felix Kluge
- Machine Learning and Data Analytics Lab, Computer Science Department, Friedrich-Alexander University, Erlangen-Nürnberg (FAU), 91052 Erlangen, Germany; (F.K.); (A.K.)
| | - Arne Küderle
- Machine Learning and Data Analytics Lab, Computer Science Department, Friedrich-Alexander University, Erlangen-Nürnberg (FAU), 91052 Erlangen, Germany; (F.K.); (A.K.)
| | - Bjoern M. Eskofier
- Machine Learning and Data Analytics Lab, Computer Science Department, Friedrich-Alexander University, Erlangen-Nürnberg (FAU), 91052 Erlangen, Germany; (F.K.); (A.K.)
| | - Diego M. López
- Telematics Engineering Research Group, Telematics Department, Universidad Del Cauca (Unicauca), Popayán 190002, Colombia;
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Polhemus AM, Bergquist R, Bosch de Basea M, Brittain G, Buttery SC, Chynkiamis N, Dalla Costa G, Delgado Ortiz L, Demeyer H, Emmert K, Garcia Aymerich J, Gassner H, Hansen C, Hopkinson N, Klucken J, Kluge F, Koch S, Leocani L, Maetzler W, Micó-Amigo ME, Mikolaizak AS, Piraino P, Salis F, Schlenstedt C, Schwickert L, Scott K, Sharrack B, Taraldsen K, Troosters T, Vereijken B, Vogiatzis I, Yarnall A, Mazza C, Becker C, Rochester L, Puhan MA, Frei A. Walking-related digital mobility outcomes as clinical trial endpoint measures: protocol for a scoping review. BMJ Open 2020; 10:e038704. [PMID: 32690539 PMCID: PMC7371223 DOI: 10.1136/bmjopen-2020-038704] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/20/2020] [Revised: 05/14/2020] [Accepted: 05/18/2020] [Indexed: 02/06/2023] Open
Abstract
INTRODUCTION Advances in wearable sensor technology now enable frequent, objective monitoring of real-world walking. Walking-related digital mobility outcomes (DMOs), such as real-world walking speed, have the potential to be more sensitive to mobility changes than traditional clinical assessments. However, it is not yet clear which DMOs are most suitable for formal validation. In this review, we will explore the evidence on discriminant ability, construct validity, prognostic value and responsiveness of walking-related DMOs in four disease areas: Parkinson's disease, multiple sclerosis, chronic obstructive pulmonary disease and proximal femoral fracture. METHODS AND ANALYSIS Arksey and O'Malley's methodological framework for scoping reviews will guide study conduct. We will search seven databases (Medline, CINAHL, Scopus, Web of Science, EMBASE, IEEE Digital Library and Cochrane Library) and grey literature for studies which (1) measure differences in DMOs between healthy and pathological walking, (2) assess relationships between DMOs and traditional clinical measures, (3) assess the prognostic value of DMOs and (4) use DMOs as endpoints in interventional clinical trials. Two reviewers will screen each abstract and full-text manuscript according to predefined eligibility criteria. We will then chart extracted data, map the literature, perform a narrative synthesis and identify gaps. ETHICS AND DISSEMINATION As this review is limited to publicly available materials, it does not require ethical approval. This work is part of Mobilise-D, an Innovative Medicines Initiative Joint Undertaking which aims to deliver, validate and obtain regulatory approval for DMOs. Results will be shared with the scientific community and general public in cooperation with the Mobilise-D communication team. REGISTRATION Study materials and updates will be made available through the Center for Open Science's OSFRegistry (https://osf.io/k7395).
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Affiliation(s)
- Ashley Marie Polhemus
- Epidemiology, Biostatistics, and Prevention Institute, University of Zürich, Zürich, Switzerland
| | - Ronny Bergquist
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Magda Bosch de Basea
- Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - Gavin Brittain
- Department of Neuroscience and Sheffield NIHR Translational Neuroscience BRC, Sheffield, UK
- Sheffield Teaching Hospitals NHS Foundation Trust & University of Sheffield, Sheffield, UK
| | | | - Nikolaos Chynkiamis
- Department of Sport, Exercise and Rehabilitation, Faculty of Health and Life Sciences, Northumbria University, Newcastle upon Tyne, Tyne and Wear, UK
| | | | - Laura Delgado Ortiz
- Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - Heleen Demeyer
- Department of Rehabilitation Sciences, KU Leuven, Leuven, Belgium
- Department of Rehabilitation Sciences, Ghent University, Ghent, Belgium
| | - Kirsten Emmert
- Department of Neurology, University Medical Center Schleswig-Holstein Campus Kiel, Kiel, Schleswig-Holstein, Germany
| | - Judith Garcia Aymerich
- Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - Heiko Gassner
- Department of Molecular Neurology, Erlangen University Hospital, Erlangen, Bayern, Germany
| | - Clint Hansen
- Department of Neurology, University Medical Center Schleswig-Holstein Campus Kiel, Kiel, Schleswig-Holstein, Germany
| | | | - Jochen Klucken
- Department of Molecular Neurology, Erlangen University Hospital, Erlangen, Bayern, Germany
| | - Felix Kluge
- Machine Learning and Data Analytics Lab, Department of Computer Science, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Bayern, Germany
| | - Sarah Koch
- Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - Letizia Leocani
- Department of Neurology, San Raffaele Hospital, Milan, Italy
| | - Walter Maetzler
- Department of Neurology, University Medical Center Schleswig-Holstein Campus Kiel, Kiel, Schleswig-Holstein, Germany
| | - M Encarna Micó-Amigo
- Translational and Clinical Research Institute, Newcastle University Faculty of Medical Sciences, Newcastle upon Tyne, Newcastle upon Tyne, UK
| | - A Stefanie Mikolaizak
- Department of Clinical Gerontology, Robert Bosch Hospital, Stuttgart, Baden-Württemberg, Germany
| | - Paolo Piraino
- Department of Research & Early Development Statistics, Bayer AG, Berlin, Germany
| | - Francesca Salis
- Department of Biomedical Sciences, University of Sassari, Sassari, Sardegna, Italy
| | - Christian Schlenstedt
- Department of Neurology, University Medical Center Schleswig-Holstein Campus Kiel, Kiel, Schleswig-Holstein, Germany
| | - Lars Schwickert
- Department of Clinical Gerontology, Robert Bosch Hospital, Stuttgart, Baden-Württemberg, Germany
| | - Kirsty Scott
- INSIGNEO Institute for in Silico Medicine, The University of Sheffield, Sheffield, Sheffield, UK
- Department of Mechanical Engineering, The University of Sheffield, Sheffield, Sheffield, UK
| | - Basil Sharrack
- Department of Neuroscience and Sheffield NIHR Translational Neuroscience BRC, Sheffield, UK
- Sheffield Teaching Hospitals NHS Foundation Trust & University of Sheffield, Sheffield, UK
| | - Kristin Taraldsen
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Thierry Troosters
- Department of Rehabilitation Sciences, KU Leuven, Leuven, Flanders, Belgium
| | - Beatrix Vereijken
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Ioannis Vogiatzis
- Department of Sport, Exercise and Rehabilitation, Faculty of Health and Life Sciences, Northumbria University, Newcastle upon Tyne, Tyne and Wear, UK
| | - Alison Yarnall
- Translational and Clinical Research Institute, Newcastle University Faculty of Medical Sciences, Newcastle upon Tyne, Newcastle upon Tyne, UK
| | - Claudia Mazza
- INSIGNEO Institute for in Silico Medicine, The University of Sheffield, Sheffield, Sheffield, UK
- Department of Mechanical Engineering, The University of Sheffield, Sheffield, Sheffield, UK
| | - Clemens Becker
- Department of Clinical Gerontology, Robert Bosch Hospital, Stuttgart, Baden-Württemberg, Germany
| | - Lynn Rochester
- Translational and Clinical Research Institute, Newcastle University Faculty of Medical Sciences, Newcastle upon Tyne, Newcastle upon Tyne, UK
| | - Milo Alan Puhan
- Epidemiology, Biostatistics, and Prevention Institute, University of Zürich, Zürich, Switzerland
| | - Anja Frei
- Epidemiology, Biostatistics, and Prevention Institute, University of Zürich, Zürich, Switzerland
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Ullrich M, Kuderle A, Hannink J, Din SD, Gasner H, Marxreiter F, Klucken J, Eskofier BM, Kluge F. Detection of Gait From Continuous Inertial Sensor Data Using Harmonic Frequencies. IEEE J Biomed Health Inform 2020; 24:1869-1878. [PMID: 32086225 DOI: 10.1109/jbhi.2020.2975361] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Mobile gait analysis using wearable inertial measurement units (IMUs) provides valuable insights for the assessment of movement impairments in different neurological and musculoskeletal diseases, for example Parkinson's disease (PD). The increase in data volume due to arising long-term monitoring requires valid, robust and efficient analysis pipelines. In many studies an upstream detection of gait is therefore applied. However, current methods do not provide a robust way to successfully reject non-gait signals. Therefore, we developed a novel algorithm for the detection of gait from continuous inertial data of sensors worn at the feet. The algorithm is focused not only on a high sensitivity but also a high specificity for gait. Sliding windows of IMU signals recorded from the feet of PD patients were processed in the frequency domain. Gait was detected if the frequency spectrum contained specific patterns of harmonic frequencies. The approach was trained and evaluated on 150 clinical measurements containing standardized gait and cyclic movement tests. The detection reached a sensitivity of 0.98 and a specificity of 0.96 for the best sensor configuration (angular rate around the medio-lateral axis). On an independent validation data set including 203 unsupervised, semi-standardized gait tests, the algorithm achieved a sensitivity of 0.97. Our algorithm for the detection of gait from continuous IMU signals works reliably and showed promising results for the application in the context of free-living and non-standardized monitoring scenarios.
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Wanner P, Schmautz T, Kluge F, Eskofier B, Pfeifer K, Steib S. Ankle angle variability during running in athletes with chronic ankle instability and copers. Gait Posture 2019; 68:329-334. [PMID: 30572182 DOI: 10.1016/j.gaitpost.2018.11.038] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/09/2018] [Revised: 11/27/2018] [Accepted: 11/30/2018] [Indexed: 02/02/2023]
Abstract
BACKGROUND Individuals with chronic ankle instability (CAI) demonstrate altered ankle kinematics during running compared to uninjured individuals; however, little is known about differences between individuals with CAI and those who recover successfully from an index sprain (copers). METHODS Thirty-two young male athletes with prior ankle sprain were investigated, eighteen with CAI and fourteen copers. Instrumented running analysis was performed on a treadmill at two velocities: moderate (2.63 ± 0.20 m/s, rate of perceived of exertion = 14/20); and high velocity (3.83 ± 0.20 m/s). Mean ankle kinematics and stride-to-stride variability were analyzed applying the statistical parametric mapping method. RESULTS At both running velocities, no statistically significant differences in mean ankle kinematics were observed. At high running velocity, athletes with CAI demonstrated significantly increased frontal plane variability at 17-19% of the running gait cycle (p = 0.009). Additionally, large between-group effect sizes (Hedges' g ≥ 0.8) may potentially indicate increased frontal plane variability during initial contact and terminal swing, as well as decreased variability in sagittal plane at 34-35% in CAI. A similar tendency existed at moderate velocity, with large effect sizes indicating decreased dorsiflexion at 75-89% in CAI, as well as an increased frontal plane variability at 16-25%, and 97-99%. DISCUSSION Compared to copers, individuals with CAI demonstrate increased variability of ankle kinematics - mainly in the frontal plane and particularly during stance phase - while mean ankle kinematics seems minimally affected. Increased ankle variability at high running velocity may best reflect persisting sensorimotor control deficits in athletes with chronically instable ankles.
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Affiliation(s)
- Philipp Wanner
- Department of Sport Science and Sport, Division of Exercise and Health, Friedrich-Alexander-University (FAU) Erlangen-Nürnberg, Gebbertstrasse 123b, 91058, Erlangen, Germany
| | - Thomas Schmautz
- Department of Sport Science and Sport, Division of Exercise and Health, Friedrich-Alexander-University (FAU) Erlangen-Nürnberg, Gebbertstrasse 123b, 91058, Erlangen, Germany
| | - Felix Kluge
- Pattern Recognition Lab, Department of Computer Science, FAU Erlangen-Nürnberg, Martensstrasse 3, 91058, Erlangen, Germany
| | - Björn Eskofier
- Pattern Recognition Lab, Department of Computer Science, FAU Erlangen-Nürnberg, Martensstrasse 3, 91058, Erlangen, Germany
| | - Klaus Pfeifer
- Department of Sport Science and Sport, Division of Exercise and Health, Friedrich-Alexander-University (FAU) Erlangen-Nürnberg, Gebbertstrasse 123b, 91058, Erlangen, Germany
| | - Simon Steib
- Department of Sport Science and Sport, Division of Exercise and Health, Friedrich-Alexander-University (FAU) Erlangen-Nürnberg, Gebbertstrasse 123b, 91058, Erlangen, Germany.
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Kluge F, Hannink J, Pasluosta C, Klucken J, Gaßner H, Gelse K, Eskofier BM, Krinner S. Pre-operative sensor-based gait parameters predict functional outcome after total knee arthroplasty. Gait Posture 2018; 66:194-200. [PMID: 30199778 DOI: 10.1016/j.gaitpost.2018.08.026] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/15/2018] [Revised: 08/20/2018] [Accepted: 08/22/2018] [Indexed: 02/02/2023]
Abstract
BACKGROUND Despite the general success of total knee arthroplasty (TKA) regarding patient-reported outcome measures, studies investigating gait function have shown diverse functional outcomes. Mobile sensor-based systems have recently been employed for accurate clinical gait assessments, as they allow a better integration of gait analysis into clinical routines as compared to laboratory based systems. RESEARCH QUESTION In this study, we sought to examine whether an accurate assessment of gait function of knee osteoarthritis patients with respect to surgery outcome evaluation after TKA using a mobile sensor-based gait analysis system is possible. METHODS A foot-worn sensor-based system was used to assess spatio-temporal gait parameters of 24 knee osteoarthritis patients one day before and one year after TKA, and in comparison to matched control participants. Patients were clustered into positive and negative responder groups using a heuristic approach regarding improvements in gait function. Machine learning was used to predict surgery outcome based on pre-operative gait parameters. RESULTS Gait function differed significantly between controls and patients. Patient-reported outcome measures improved significantly after surgery, but no significant global gait parameter difference was observed between pre- and post-operative status. However, the responder groups could be correctly predicted with an accuracy of up to 89% using pre-operative gait parameters. Patients exhibiting high pre-operative gait function were more likely to experience a functional decrease after surgery. Important gait parameters for the discrimination were stride time and stride length. SIGNIFICANCE The early identification of post-surgical functional outcomes of patients is of great importance to better inform patients pre-operatively regarding surgery success and to improve post-surgical management.
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Affiliation(s)
- Felix Kluge
- Machine Learning and Data Analytics Lab, Department of Computer Science, Friedrich-Alexander University Erlangen-Nürnberg (FAU), Carl-Thiersch-Str. 2b, 91052 Erlangen, Germany.
| | - Julius Hannink
- Machine Learning and Data Analytics Lab, Department of Computer Science, Friedrich-Alexander University Erlangen-Nürnberg (FAU), Carl-Thiersch-Str. 2b, 91052 Erlangen, Germany.
| | - Cristian Pasluosta
- Laboratory for Biomedical Microtechnology, Department of Microsystems Engineering, University of Freiburg, 79110 Freiburg, Germany.
| | - Jochen Klucken
- Molecular Neurology, University Hospital Erlangen, Schwabachanlage 6, 91054 Erlangen, Germany.
| | - Heiko Gaßner
- Molecular Neurology, University Hospital Erlangen, Schwabachanlage 6, 91054 Erlangen, Germany.
| | - Kolja Gelse
- Department of Trauma Surgery, University Hospital Erlangen, Krankenhausstrasse 12, 91054 Erlangen, Germany.
| | - Bjoern M Eskofier
- Machine Learning and Data Analytics Lab, Department of Computer Science, Friedrich-Alexander University Erlangen-Nürnberg (FAU), Carl-Thiersch-Str. 2b, 91052 Erlangen, Germany.
| | - Sebastian Krinner
- Department of Trauma Surgery, University Hospital Erlangen, Krankenhausstrasse 12, 91054 Erlangen, Germany.
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Knorz S, Kluge F, Gelse K, Schulz-Drost S, Hotfiel T, Lochmann M, Eskofier B, Krinner S. Three-Dimensional Biomechanical Analysis of Rearfoot and Forefoot Running. Orthop J Sports Med 2017; 5:2325967117719065. [PMID: 28812039 PMCID: PMC5528965 DOI: 10.1177/2325967117719065] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Background: In the running community, a forefoot strike (FFS) pattern is increasingly preferred compared with a rearfoot strike (RFS) pattern. However, it has not been fully understood which strike pattern may better reduce adverse joint forces within the different joints of the lower extremity. Purpose: To analyze the 3-dimensional (3D) stress pattern in the ankle, knee, and hip joint in runners with either a FFS or RFS pattern. Study Design: Descriptive laboratory study. Methods: In 22 runners (11 habitual rearfoot strikers, 11 habitual forefoot strikers), RFS and FFS patterns were compared at 3.0 m/s (6.7 mph) on a treadmill with integrated force plates and a 3D motion capture analysis system. This combined analysis allowed characterization of the 3D biomechanical forces differentiated for the ankle, knee, and hip joint. The maximum peak force (MPF) and maximum loading rate (LR) were determined in their 3 ordinal components: vertical, anterior-posterior (AP), and medial-lateral (ML). Results: For both strike patterns, the vertical components of the MPF and LR were significantly greater than their AP or ML components. In the vertical axis, FFS was generally associated with a greater MPF but significantly lower LR in all 3 joints. The AP components of MPF and LR were significantly lower for FFS in the knee joint but significantly greater in the ankle and hip joints. The ML components of MPF and LR tended to be greater for FFS but mostly did not reach a level of significance. Conclusion: FFS and RFS were associated with different 3D stress patterns in the ankle, knee, and hip joint, although there was no global advantage of one strike pattern over the other. The multimodal individual assessment for the different anatomic regions demonstrated that FFS seems favorable for patients with unstable knee joints in the AP axis and RFS may be recommended for runners with unstable ankle joints. Clinical Relevance: Different strike patterns show different 3D stress in joints of the lower extremity. Due to either rehabilitation after injuries or training in running sports, rearfoot or forefoot running should be preferred to prevent further damage or injuries caused by inadequate biomechanical load. Runners with a history of knee joint injuries may benefit from FFS whereas RFS may be favorable for runners with a history of ankle joint injuries.
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Affiliation(s)
- Sebastian Knorz
- Department of Orthopaedic and Trauma Surgery, University Hospital Erlangen, Friedrich-Alexander-University Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Felix Kluge
- Department of Orthopaedic and Trauma Surgery, University Hospital Erlangen, Friedrich-Alexander-University Erlangen-Nürnberg (FAU), Erlangen, Germany.,Department of Sports and Exercise Medicine, Friedrich-Alexander-University Erlangen-Nürnberg (FAU), Erlangen, Germany.,Department of Computer Science, Pattern Recognition Lab, Friedrich-Alexander-University Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Kolja Gelse
- Department of Orthopaedic and Trauma Surgery, University Hospital Erlangen, Friedrich-Alexander-University Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Stefan Schulz-Drost
- Department of Orthopaedic and Trauma Surgery, University Hospital Erlangen, Friedrich-Alexander-University Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Thilo Hotfiel
- Department of Orthopaedic Rheumatology, Friedrich-Alexander-University Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Matthias Lochmann
- Department of Sports and Exercise Medicine, Friedrich-Alexander-University Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Björn Eskofier
- Department of Computer Science, Pattern Recognition Lab, Friedrich-Alexander-University Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Sebastian Krinner
- Department of Orthopaedic and Trauma Surgery, University Hospital Erlangen, Friedrich-Alexander-University Erlangen-Nürnberg (FAU), Erlangen, Germany
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Kluge F, Gaßner H, Hannink J, Pasluosta C, Klucken J, Eskofier BM. Towards Mobile Gait Analysis: Concurrent Validity and Test-Retest Reliability of an Inertial Measurement System for the Assessment of Spatio-Temporal Gait Parameters. Sensors (Basel) 2017; 17:s17071522. [PMID: 28657587 PMCID: PMC5539856 DOI: 10.3390/s17071522] [Citation(s) in RCA: 87] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/30/2017] [Revised: 06/20/2017] [Accepted: 06/23/2017] [Indexed: 12/26/2022]
Abstract
The purpose of this study was to assess the concurrent validity and test–retest reliability of a sensor-based gait analysis system. Eleven healthy subjects and four Parkinson’s disease (PD) patients were asked to complete gait tasks whilst wearing two inertial measurement units at their feet. The extracted spatio-temporal parameters of 1166 strides were compared to those extracted from a reference camera-based motion capture system concerning concurrent validity. Test–retest reliability was assessed for five healthy subjects at three different days in a two week period. The two systems were highly correlated for all gait parameters (r>0.93). The bias for stride time was 0±16 ms and for stride length was 1.4±6.7 cm. No systematic range dependent errors were observed and no significant changes existed between healthy subjects and PD patients. Test-retest reliability was excellent for all parameters (intraclass correlation (ICC) > 0.81) except for gait velocity (ICC > 0.55). The sensor-based system was able to accurately capture spatio-temporal gait parameters as compared to the reference camera-based system for normal and impaired gait. The system’s high retest reliability renders the use in recurrent clinical measurements and in long-term applications feasible.
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Affiliation(s)
- Felix Kluge
- Digital Sports Group, Pattern Recognition Laboratory, Department of Computer Science, Friedrich-Alexander University Erlangen-Nürnberg (FAU), 91058 Erlangen, Germany.
| | - Heiko Gaßner
- Molecular Neurology, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nürnberg (FAU), 91054 Erlangen, Germany.
| | - Julius Hannink
- Digital Sports Group, Pattern Recognition Laboratory, Department of Computer Science, Friedrich-Alexander University Erlangen-Nürnberg (FAU), 91058 Erlangen, Germany.
| | - Cristian Pasluosta
- Digital Sports Group, Pattern Recognition Laboratory, Department of Computer Science, Friedrich-Alexander University Erlangen-Nürnberg (FAU), 91058 Erlangen, Germany.
- Laboratory for Biomedical Microtechnology, Department of Microsystems Engineering, University of Freiburg, 79110 Freiburg, Germany.
| | - Jochen Klucken
- Molecular Neurology, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nürnberg (FAU), 91054 Erlangen, Germany.
| | - Björn M Eskofier
- Digital Sports Group, Pattern Recognition Laboratory, Department of Computer Science, Friedrich-Alexander University Erlangen-Nürnberg (FAU), 91058 Erlangen, Germany.
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Kluge F, Krinner S, Lochmann M, Eskofier BM. Speed dependent effects of laterally wedged insoles on gait biomechanics in healthy subjects. Gait Posture 2017; 55:145-149. [PMID: 28445855 DOI: 10.1016/j.gaitpost.2017.04.012] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/18/2017] [Revised: 03/29/2017] [Accepted: 04/07/2017] [Indexed: 02/02/2023]
Abstract
Laterally wedged insoles have been shown to be effective for the reduction of the knee adduction moment and other biomechanical variables that are associated with the pathogenesis of knee osteoarthritis. However, inconclusive results such as adverse effects in individual subjects or even no group-wise wedge effects have been presented in different studies and it has been suggested to identify variables that potentially confound the wedge effect. The main objective of this study was the investigation of interaction effects of lateral wedges with walking speed, as different self-selected speeds have mainly been used in previous studies. Twenty-two healthy subjects completed gait analysis trials on an instrumented treadmill. They walked in different speed conditions (0.9, 1.1, 1.3, 1.5m/s) with a neutral and a laterally wedged insole. Kinematics were acquired using infrared cinematography with reflective markers attached to the lower body. From the stance phase we extracted biomechanical parameters that are associated with knee joint loading and osteoarthritis severity. No interaction effect of lateral wedges and speed was observed for most biomechanical parameters except for the ankle eversion range of motion. The main effects of wedges were reductions of the external knee adduction moment and of the knee adduction angular impulse. All biomechanical variables changed with increasing speed. Only the lateral offset of the center of pressure did not respond to wedge or to speed changes. Our results suggest that different self-selected speeds do not confound the effect of laterally wedged insoles.
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Affiliation(s)
- Felix Kluge
- Digital Sports Group, Pattern Recognition Lab, Department of Computer Science, Friedrich-Alexander University Erlangen-Nürnberg (FAU), Martensstrasse 3, 91058 Erlangen, Germany; Institute of Sport Science and Sport, Friedrich-Alexander University Erlangen-Nürnberg (FAU), Gebbertstraße 123B, 91058 Erlangen, Germany.
| | - Sebastian Krinner
- Department of Trauma Surgery, University Hospital Erlangen, Krankenhausstraße 12, 91054 Erlangen, Germany.
| | - Matthias Lochmann
- Institute of Sport Science and Sport, Friedrich-Alexander University Erlangen-Nürnberg (FAU), Gebbertstraße 123B, 91058 Erlangen, Germany.
| | - Bjoern M Eskofier
- Digital Sports Group, Pattern Recognition Lab, Department of Computer Science, Friedrich-Alexander University Erlangen-Nürnberg (FAU), Martensstrasse 3, 91058 Erlangen, Germany.
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Kluge F, Eskofier BM. Letter to the Editor regarding "Gait recording with inertial sensors - How to determine initial and terminal contact" by Bötzel and colleagues. J Biomech 2017; 52:183-184. [PMID: 28010948 DOI: 10.1016/j.jbiomech.2016.07.043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2016] [Accepted: 07/28/2016] [Indexed: 11/24/2022]
Affiliation(s)
- Felix Kluge
- Digital Sports Group, Pattern Recognition Lab, Department of Computer Science, Friedrich-Alexander University Erlangen-Nürnberg (FAU), Immerwahrstrasse 2a, 91058 Erlangen, Germany.
| | - Björn M Eskofier
- Digital Sports Group, Pattern Recognition Lab, Department of Computer Science, Friedrich-Alexander University Erlangen-Nürnberg (FAU), Immerwahrstrasse 2a, 91058 Erlangen, Germany.
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Benesic A, Zilly M, Kluge F, Weissbrich B, Winzer R, Klinker H, Langmann P. Lipid Lowering Therapy with Fluvastatin and Pravastatin in Patients with HIV Infection and Antiretroviral Therapy: Comparison of Efficacy and Interaction with Indinavir. Infection 2004; 32:229-33. [PMID: 15293079 DOI: 10.1007/s15010-004-3136-7] [Citation(s) in RCA: 41] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2003] [Accepted: 02/12/2004] [Indexed: 10/26/2022]
Abstract
BACKGROUND Lipoprotein disorders in HIV-positive patients receiving highly active antiretroviral therapy (HAART) are becoming a major concern in HIV treatment, since there is growing evidence for an association between HAART-induced hyperlipidemia and increased cardiovascular risk. Yet relatively few data are available on the possible interactions of HAART and treatment with statins. PATIENTS AND METHODS In this prospective study, 25 HIV-positive, treatment-experienced patients (five female, 20 male, all Caucasian) were treated with either fluvastatin or pravastatin. Total cholesterol, low density lipoprotein (LDL) and high density lipoprotein (HDL) levels, and serum triglycerides were determined at regular intervals, as well as therapeutic drug monitoring to assess possible drug interactions. RESULTS In 13 pravastatin-treated patients, a decrease in total cholesterol levels (from 7.12 mmol/l to 6.29 mmol/l) after 12 weeks of therapy was seen. In 12 patients treated with fluvastatin, a permanent reduction of total cholesterol (from 6.46 mmol/l to 5.31 mmol/l) after 12 weeks was observed. The reduction of LDL levels was 30.2% in the fluvastatin group and 14.4% in the pravastatin group. In eight patients receiving an indinavir-containing HAART, indinavir plasma levels were not significantly influenced. No effect on triglycerides or HDL was observed. CONCLUSION Fluvastatin and pravastatin are efficient in lowering total and LDL cholesterol levels in HIV-positive patients receiving HAART. Furthermore, no influence on indinavir plasma levels could be observed. Therefore, both compounds seem to be a viable treatment option in HAART-induced hypercholesterolemia.
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Affiliation(s)
- A Benesic
- Division of Infectious Diseases, Dept. of Internal Medicine, University of Würzburg, Josef-Schneider-Str. 2, D-97080 Würzburg, Germany.
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Freund U, Schölmerich J, Siems H, Kluge F, Schäfer HE, Wannenmacher M. [Unwanted side-effects in using mesalazine (5-aminosalicylic acid) during radiotherapy]. Strahlenther Onkol 1987; 163:678-80. [PMID: 3313776] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
In a prospective randomized placebo controlled double blind study, the prophylactic effect of mesalazine (5-aminosalicylic acid. 5-ASA) as suppositories (3 x 250 mg/day) on radiation induced proctitis during radiotherapy for prostatic carcinoma was studied. The study ended when 16 patients had been included (5-ASA: eight, placebo: eight) because of severe side effects in the 5-ASA group. 75% of patients treated with 5-ASA reported symptoms of a severe proctitis while only one patient in the placebo group had similar complaints. The application of mesalazine as suppositories is not useful in preventing radiation induced proctitis during radiotherapy of prostate carcinoma.
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Affiliation(s)
- U Freund
- Abteilung Röntgen- und Strahlentherapie, Universitätsklinik Freiburg
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Kluge F. [120 years of gastroenterological endoscopy--who was the first?]. Leber Magen Darm 1987; 17:217-8. [PMID: 3312883] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
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Kuntze M, Kluge F, Schümichen C. [Diagnosis of pheochromocytoma]. Lebensversicher Med 1987; 39:110-4. [PMID: 2887991] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
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Senn HJ, Ehret W, Kluge F, Scholz K, Billmann P, Ruckdeschel G. A case of pneumonia caused by Legionella pneumophila serogroup 8 in West Germany. Etiological diagnosis by immunofluorescence, enzyme immunoassay, and immunoblot. Klin Wochenschr 1987; 65:138-43. [PMID: 3553722 DOI: 10.1007/bf01728607] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
The severe and complicated course of a Legionella pneumonia in an 18-year-old female patient is reported. The serogroup 8 of Legionella pneumophila was identified as the infecting agent for the first time in West Germany. The etiological diagnosis was confirmed by immunofluorescence, enzyme immunoassay, and immunoblot. We emphasize the importance of serological testing of all Legionella pneumophila serogroups available.
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Kluge F, Seidler E. [Not Available]. Medizinhist J 1986; 21:288-307. [PMID: 11637391] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 05/23/2023]
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Kluge F, Hoppe-Seyler P, Gerok W. [Immunogenetic findings in Crohn disease, ulcerative colitis and gluten sensitive enteropathy]. Z Gastroenterol 1984; 22:277-84. [PMID: 6464520] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
Immunogenetics explain the influence of genetic dispositions and regulation mechanisms on the defence mechanisms of organisms. In this paper the interrelationship between immunogenetic findings and Crohn's disease, ulcerative colitis and glutensensitive enteropathy are presented. The association with HLA-antigens and the complement factors Bf and C 4 are described. An increased association of HLA-antigens with Crohn's disease and ulcerative colitis have not been observed, with one exception, the association between HLA-Bw 35 (53%) and ulcerative colitis in a particular ethnic group in Israel. In contrast, there is a distinct association between the complement allotype Bf-F and Crohn's disease (68%) and ulcerative colitis (50%), compared with controls (29%). Glutensensitive enteropathy shows no increased occurrence of complement factors however there is an association with HLA-B8 (83%) DR 3 (94%) and DR 7 (61%). The phenotype description of the genes of the main histocompatibility complexes allows, amongst others, a conclusion concerning the pathogenesis and diagnosis of certain diseases. A new approach to these diseases has been introduced.
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