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Filippou V, Backhouse MR, Redmond AC, Wong DC. Person-Specific Template Matching Using a Dynamic Time Warping Step-Count Algorithm for Multiple Walking Activities. SENSORS (BASEL, SWITZERLAND) 2023; 23:9061. [PMID: 38005449 PMCID: PMC10675039 DOI: 10.3390/s23229061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Revised: 10/22/2023] [Accepted: 11/07/2023] [Indexed: 11/26/2023]
Abstract
This study aimed to develop and evaluate a new step-count algorithm, StepMatchDTWBA, for the accurate measurement of physical activity using wearable devices in both healthy and pathological populations. We conducted a study with 30 healthy volunteers wearing a wrist-worn MOX accelerometer (Maastricht Instruments, NL). The StepMatchDTWBA algorithm used dynamic time warping (DTW) barycentre averaging to create personalised templates for representative steps, accounting for individual walking variations. DTW was then used to measure the similarity between the template and accelerometer epoch. The StepMatchDTWBA algorithm had an average root-mean-square error of 2 steps for healthy gaits and 12 steps for simulated pathological gaits over a distance of about 10 m (GAITRite walkway) and one flight of stairs. It outperformed benchmark algorithms for the simulated pathological population, showcasing the potential for improved accuracy in personalised step counting for pathological populations. The StepMatchDTWBA algorithm represents a significant advancement in accurate step counting for both healthy and pathological populations. This development holds promise for creating more precise and personalised activity monitoring systems, benefiting various health and wellness applications.
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Affiliation(s)
- Valeria Filippou
- Institute of Medical and Biological Engineering, University of Leeds, Leeds LS2 9JT, UK
| | | | - Anthony C. Redmond
- Leeds Institute of Rheumatic and Musculoskeletal Medicine, University of Leeds, Leeds LS2 9JT, UK;
| | - David C. Wong
- Leeds Institute of Health Informatics, University of Leeds, Leeds LS2 9JT, UK;
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2
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van Gelder LMA, Bonci T, Buckley EE, Price K, Salis F, Hadjivassiliou M, Mazzà C, Hewamadduma C. A Single-Sensor Approach to Quantify Gait in Patients with Hereditary Spastic Paraplegia. SENSORS (BASEL, SWITZERLAND) 2023; 23:6563. [PMID: 37514857 PMCID: PMC10384193 DOI: 10.3390/s23146563] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Revised: 07/10/2023] [Accepted: 07/17/2023] [Indexed: 07/30/2023]
Abstract
Hereditary spastic paraplegia (HSP) is characterised by progressive lower-limb spasticity and weakness resulting in ambulation difficulties. During clinical practice, walking is observed and/or assessed by timed 10-metre walk tests; time, feasibility, and methodological reliability are barriers to detailed characterisation of patients' walking abilities when instrumenting this test. Wearable sensors have the potential to overcome such drawbacks once a validated approach is available for patients with HSP. Therefore, while limiting patients' and assessors' burdens, this study aims to validate the adoption of a single lower-back wearable inertial sensor approach for step detection in HSP patients; this is the first essential algorithmic step in quantifying most gait temporal metrics. After filtering the 3D acceleration signal based on its smoothness and enhancing the step-related peaks, initial contacts (ICs) were identified as positive zero-crossings of the processed signal. The proposed approach was validated on thirteen individuals with HSP while they performed three 10-metre tests and wore pressure insoles used as a gold standard. Overall, the single-sensor approach detected 794 ICs (87% correctly identified) with high accuracy (median absolute errors (mae): 0.05 s) and excellent reliability (ICC = 1.00). Although about 12% of the ICs were missed and the use of walking aids introduced extra ICs, a minor impact was observed on the step time quantifications (mae 0.03 s (5.1%), ICC = 0.89); the use of walking aids caused no significant differences in the average step time quantifications. Therefore, the proposed single-sensor approach provides a reliable methodology for step identification in HSP, augmenting the gait information that can be accurately and objectively extracted from patients with HSP during their clinical assessment.
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Affiliation(s)
- Linda M A van Gelder
- Department of Mechanical Engineering, INSIGNEO Institute for In Silico Medicine, The University of Sheffield, Sheffield S10 2TN, UK
| | - Tecla Bonci
- Department of Mechanical Engineering, INSIGNEO Institute for In Silico Medicine, The University of Sheffield, Sheffield S10 2TN, UK
| | - Ellen E Buckley
- Department of Mechanical Engineering, INSIGNEO Institute for In Silico Medicine, The University of Sheffield, Sheffield S10 2TN, UK
| | - Kathryn Price
- Academic Department of Neurosciences, Sheffield Teaching Hospitals NHS Trust, University of Sheffield, Sheffield S10 2TN, UK
| | - Francesca Salis
- Department of Biomedical Sciences, University of Sassari, 07100 Sassari, Italy
| | - Marios Hadjivassiliou
- Academic Department of Neurosciences, Sheffield Teaching Hospitals NHS Trust, University of Sheffield, Sheffield S10 2TN, UK
| | - Claudia Mazzà
- Department of Mechanical Engineering, INSIGNEO Institute for In Silico Medicine, The University of Sheffield, Sheffield S10 2TN, UK
| | - Channa Hewamadduma
- Academic Department of Neurosciences, Sheffield Teaching Hospitals NHS Trust, University of Sheffield, Sheffield S10 2TN, UK
- The Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield S10 2TN, UK
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3
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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] [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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4
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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] [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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5
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Bach MM, Dominici N, Daffertshofer A. Predicting vertical ground reaction forces from 3D accelerometry using reservoir computers leads to accurate gait event detection. Front Sports Act Living 2022; 4:1037438. [PMID: 36385782 PMCID: PMC9644164 DOI: 10.3389/fspor.2022.1037438] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Accepted: 10/04/2022] [Indexed: 11/06/2022] Open
Abstract
Accelerometers are low-cost measurement devices that can readily be used outside the lab. However, determining isolated gait events from accelerometer signals, especially foot-off events during running, is an open problem. We outline a two-step approach where machine learning serves to predict vertical ground reaction forces from accelerometer signals, followed by force-based event detection. We collected shank accelerometer signals and ground reaction forces from 21 adults during comfortable walking and running on an instrumented treadmill. We trained one common reservoir computer using segmented data using both walking and running data. Despite being trained on just a small number of strides, this reservoir computer predicted vertical ground reaction forces in continuous gait with high quality. The subsequent foot contact and foot off event detection proved highly accurate when compared to the gold standard based on co-registered ground reaction forces. Our proof-of-concept illustrates the capacity of combining accelerometry with machine learning for detecting isolated gait events irrespective of mode of locomotion.
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6
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Bursais AK, Gentles JA, Albujulaya NM, Stone MH. Field based assessment of a tri-axial accelerometers validity to identify steps and reliability to quantify external load. Front Physiol 2022; 13:942954. [PMID: 36171976 PMCID: PMC9510681 DOI: 10.3389/fphys.2022.942954] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Accepted: 08/19/2022] [Indexed: 11/25/2022] Open
Abstract
Background: The monitoring of accelerometry derived load has received increased attention in recent years. However, the ability of such measures to quantify training load during sport-related activities is not well established. Thus, the current study aimed to assess the validity and reliability of tri-axial accelerometers to identify step count and quantify external load during several locomotor conditions including walking, jogging, and running. Method: Thirty physically active college students (height = 176.8 ± 6.1 cm, weight = 82.3 ± 12.8 kg) participated. Acceleration data was collected via two tri-axial accelerometers (Device A and B) sampling at 100 Hz, mounted closely together at the xiphoid process. Each participant completed two trials of straight-line walking, jogging, and running on a 20 m course. Device A was used to assess accelerometer validity to identify step count and the test-retest reliability of the instrument to quantify the external load. Device A and Device B were used to assess inter-device reliability. The reliability of accelerometry-derived metrics Impulse Load (IL) and Magnitude g (MAG) were assessed. Results: The instrument demonstrated a positive predictive value (PPV) ranging between 96.98%–99.41% and an agreement ranging between 93.08%–96.29% for step detection during all conditions. Good test-retest reliability was found with a coefficient of variation (CV) <5% for IL and MAG during all locomotor conditions. Good inter-device reliability was also found for all locomotor conditions (IL and MAG CV < 5%). Conclusion: This research indicates that tri-axial accelerometers can be used to identify steps and quantify external load when movement is completed at a range of speeds.
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Affiliation(s)
- Abdulmalek K. Bursais
- Department of Physical Education, College of Education, King Faisal University, Al-Ahsa, Saudi Arabia
- Center of Excellence for Sport Science and Coach Education, East Tennessee State University, Johnson, TN, United States
- *Correspondence: Abdulmalek K. Bursais,
| | - Jeremy A. Gentles
- Center of Excellence for Sport Science and Coach Education, East Tennessee State University, Johnson, TN, United States
| | - Naif M. Albujulaya
- Department of Physical Education, College of Education, King Faisal University, Al-Ahsa, Saudi Arabia
- School of Sport, Exercise and Health Sciences, Loughborough University, Loughborough, Leicestershire, United Kingdom
| | - Michael H. Stone
- Center of Excellence for Sport Science and Coach Education, East Tennessee State University, Johnson, TN, United States
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7
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Luu L, Pillai A, Lea H, Buendia R, Khan FM, Dennis G. Accurate Step Count with Generalized and Personalized Deep Learning on Accelerometer Data. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22113989. [PMID: 35684609 PMCID: PMC9183122 DOI: 10.3390/s22113989] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Revised: 05/05/2022] [Accepted: 05/20/2022] [Indexed: 05/15/2023]
Abstract
Physical activity (PA) is globally recognized as a pillar of general health. Step count, as one measure of PA, is a well known predictor of long-term morbidity and mortality. Despite its popularity in consumer devices, a lack of methodological standards and clinical validation remains a major impediment to step count being accepted as a valid clinical endpoint. Previous works have mainly focused on device-specific step-count algorithms and often employ sensor modalities that may not be widely available. This may limit step-count suitability in clinical scenarios. In this paper, we trained neural network models on publicly available data and tested on an independent cohort using two approaches: generalization and personalization. Specifically, we trained neural networks on accelerometer signals from one device and either directly applied them or adapted them individually to accelerometer data obtained from a separate subject cohort wearing multiple distinct devices. The best models exhibited highly accurate step-count estimates for both the generalization (96-99%) and personalization (98-99%) approaches. The results demonstrate that it is possible to develop device-agnostic, accelerometer-only algorithms that provide highly accurate step counts, positioning step count as a reliable mobility endpoint and a strong candidate for clinical validation.
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Affiliation(s)
- Long Luu
- Digital Health, Oncology R&D, AstraZeneca, Gaithersburg, MD 20878, USA;
- Correspondence:
| | - Arvind Pillai
- Department of Computer Science, Dartmouth College, Hanover, NH 03755, USA;
| | - Halsey Lea
- Digital Health, Oncology R&D, AstraZeneca, Gaithersburg, MD 20878, USA;
| | - Ruben Buendia
- Biometrics, Late-Stage Development, Cardiovascular, Renal and Metabolism (CVRM), BioPharmaceuticals R&D, AstraZeneca, 43183 Gothenburg, Sweden;
| | - Faisal M. Khan
- AI & Analytics, Data Science & Artificial Intelligence R&D, AstraZeneca, Gaithersburg, MD 20878, USA; (F.M.K.); (G.D.)
| | - Glynn Dennis
- AI & Analytics, Data Science & Artificial Intelligence R&D, AstraZeneca, Gaithersburg, MD 20878, USA; (F.M.K.); (G.D.)
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8
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Hellec J, Chorin F, Castagnetti A, Guérin O, Colson SS. Smart Eyeglasses: A Valid and Reliable Device to Assess Spatiotemporal Parameters during Gait. SENSORS (BASEL, SWITZERLAND) 2022; 22:1196. [PMID: 35161941 PMCID: PMC8846265 DOI: 10.3390/s22031196] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Revised: 01/31/2022] [Accepted: 02/02/2022] [Indexed: 12/14/2022]
Abstract
The study aims to determine the validity and reproducibility of step duration and step length parameters measured during walking in healthy participants using an accelerometer embedded in smart eyeglasses. Twenty young volunteers participated in two identical sessions comprising a 30 s gait assessment performed at three different treadmill speeds under two conditions (i.e., with and without a cervical collar). Spatiotemporal parameters (i.e., step duration and step length normalized by the lower limb length) were obtained with both the accelerometer embedded in smart eyeglasses and an optoelectronic system. The relative intra- and inter-session reliability of step duration and step length computed from the vertical acceleration data were excellent for all experimental conditions. An excellent absolute reliability was observed for the eyeglasses for all conditions and concurrent validity between systems was observed. An accelerometer incorporated in smart eyeglasses is accurate to measure step duration and step length during gait.
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Affiliation(s)
- Justine Hellec
- Université Côte d’Azur, LAMHESS, EUR HEALTHY, 06205 Nice, France; (F.C.); (S.S.C.)
- Ellcie Healthy, 06600 Antibes, France
| | - Frédéric Chorin
- Université Côte d’Azur, LAMHESS, EUR HEALTHY, 06205 Nice, France; (F.C.); (S.S.C.)
- Université Côte d’Azur, CHU, Cimiez, Plateforme Fragilité, 06000 Nice, France
| | | | | | - Serge S. Colson
- Université Côte d’Azur, LAMHESS, EUR HEALTHY, 06205 Nice, France; (F.C.); (S.S.C.)
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9
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Felius RAW, Geerars M, Bruijn SM, van Dieën JH, Wouda NC, Punt M. Reliability of IMU-Based Gait Assessment in Clinical Stroke Rehabilitation. SENSORS 2022; 22:s22030908. [PMID: 35161654 PMCID: PMC8839370 DOI: 10.3390/s22030908] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Revised: 01/16/2022] [Accepted: 01/19/2022] [Indexed: 02/06/2023]
Abstract
Background: Gait is often impaired in people after stroke, restricting personal independence and affecting quality of life. During stroke rehabilitation, walking capacity is conventionally assessed by measuring walking distance and speed. Gait features, such as asymmetry and variability, are not routinely determined, but may provide more specific insights into the patient’s walking capacity. Inertial measurement units offer a feasible and promising tool to determine these gait features. Objective: We examined the test–retest reliability of inertial measurement units-based gait features measured in a two-minute walking assessment in people after stroke and while in clinical rehabilitation. Method: Thirty-one people after stroke performed two assessments with a test–retest interval of 24 h. Each assessment consisted of a two-minute walking test on a 14-m walking path. Participants were equipped with three inertial measurement units, placed at both feet and at the low back. In total, 166 gait features were calculated for each assessment, consisting of spatio-temporal (56), frequency (26), complexity (63), and asymmetry (14) features. The reliability was determined using the intraclass correlation coefficient. Additionally, the minimal detectable change and the relative minimal detectable change were computed. Results: Overall, 107 gait features had good–excellent reliability, consisting of 50 spatio-temporal, 8 frequency, 36 complexity, and 13 symmetry features. The relative minimal detectable change of these features ranged between 0.5 and 1.5 standard deviations. Conclusion: Gait can reliably be assessed in people after stroke in clinical stroke rehabilitation using three inertial measurement units.
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Affiliation(s)
- Richard A. W. Felius
- Research Group Lifestyle and Health, Utrecht University of Applied Sciences, 3584 CS Utrecht, The Netherlands; (M.G.); (N.C.W.); (M.P.)
- Faculty of Human Movement Sciences, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands; (S.M.B.); (J.H.v.D.)
- Correspondence:
| | - Marieke Geerars
- Research Group Lifestyle and Health, Utrecht University of Applied Sciences, 3584 CS Utrecht, The Netherlands; (M.G.); (N.C.W.); (M.P.)
- Physiotherapy Department Neurology, Rehabilitation Center de Parkgraaf, 3526 KJ Utrecht, The Netherlands
| | - Sjoerd M. Bruijn
- Faculty of Human Movement Sciences, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands; (S.M.B.); (J.H.v.D.)
| | - Jaap H. van Dieën
- Faculty of Human Movement Sciences, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands; (S.M.B.); (J.H.v.D.)
| | - Natasja C. Wouda
- Research Group Lifestyle and Health, Utrecht University of Applied Sciences, 3584 CS Utrecht, The Netherlands; (M.G.); (N.C.W.); (M.P.)
- Physiotherapy Department Neurology, De Hoogstraat Revalidatie, 3583 TM Utrecht, The Netherlands
| | - Michiel Punt
- Research Group Lifestyle and Health, Utrecht University of Applied Sciences, 3584 CS Utrecht, The Netherlands; (M.G.); (N.C.W.); (M.P.)
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10
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Davis JJ, Straczkiewicz M, Harezlak J, Gruber AH. CARL: a running recognition algorithm for free-living accelerometer data. Physiol Meas 2021; 42. [PMID: 34883471 DOI: 10.1088/1361-6579/ac41b8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Accepted: 12/09/2021] [Indexed: 11/11/2022]
Abstract
Wearable accelerometers hold great promise for physical activity epidemiology and sports biomechanics. However, identifying and extracting data from specific physical activities, such as running, remains challenging.Objective. To develop and validate an algorithm to identify bouts of running in raw, free-living accelerometer data from devices worn at the wrist or torso (waist, hip, chest).Approach. The CARL (continuous amplitude running logistic) classifier identifies acceleration data with amplitude and frequency characteristics consistent with running. The CARL classifier was trained on data from 31 adults wearing accelerometers on the waist and wrist, then validated on free-living data from 30 new, unseen subjects plus 166 subjects from previously-published datasets using different devices, wear locations, and sample frequencies.Main results. On free-living data, the CARL classifier achieved mean accuracy (F1score) of 0.984 (95% confidence interval 0.962-0.996) for data from the waist and 0.994 (95% CI 0.991-0.996) for data from the wrist. In previously-published datasets, the CARL classifier identified running with mean accuracy (F1score) of 0.861 (95% CI 0.836-0.884) for data from the chest, 0.911 (95% CI 0.884-0.937) for data from the hip, 0.916 (95% CI 0.877-0.948) for data from the waist, and 0.870 (95% CI 0.834-0.903) for data from the wrist. Misclassification primarily occurred during activities with similar torso acceleration profiles to running, such as rope jumping and elliptical machine use.Significance. The CARL classifier can accurately identify bouts of running as short as three seconds in free-living accelerometry data. An open-source implementation of the CARL classifier is available atgithub.com/johnjdavisiv/carl.
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Affiliation(s)
- John J Davis
- Department of Kinesiology, School of Public Health, Indiana University Bloomington, Bloomington, IN United States of America
| | - Marcin Straczkiewicz
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA United States of America
| | - Jaroslaw Harezlak
- Department of Epidemiology and Biostatistics, School of Public Health, Indiana University Bloomington, Bloomington, IN United States of America
| | - Allison H Gruber
- Department of Kinesiology, School of Public Health, Indiana University Bloomington, Bloomington, IN United States of America
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11
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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. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 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] [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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12
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Rochester L, Mazzà C, Mueller A, Caulfield B, McCarthy M, Becker C, Miller R, Piraino P, Viceconti M, Dartee WP, Garcia-Aymerich J, Aydemir AA, Vereijken B, Arnera V, Ammour N, Jackson M, Hache T, Roubenoff R. A Roadmap to Inform Development, Validation and Approval of Digital Mobility Outcomes: The Mobilise-D Approach. Digit Biomark 2020; 4:13-27. [PMID: 33442578 DOI: 10.1159/000512513] [Citation(s) in RCA: 54] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Accepted: 10/23/2020] [Indexed: 12/19/2022] Open
Abstract
Health care has had to adapt rapidly to COVID-19, and this in turn has highlighted a pressing need for tools to facilitate remote visits and monitoring. Digital health technology, including body-worn devices, offers a solution using digital outcomes to measure and monitor disease status and provide outcomes meaningful to both patients and health care professionals. Remote monitoring of physical mobility is a prime example, because mobility is among the most advanced modalities that can be assessed digitally and remotely. Loss of mobility is also an important feature of many health conditions, providing a read-out of health as well as a target for intervention. Real-world, continuous digital measures of mobility (digital mobility outcomes or DMOs) provide an opportunity for novel insights into health care conditions complementing existing mobility measures. Accepted and approved DMOs are not yet widely available. The need for large collaborative efforts to tackle the critical steps to adoption is widely recognised. Mobilise-D is an example. It is a multidisciplinary consortium of 34 institutions from academia and industry funded through the European Innovative Medicines Initiative 2 Joint Undertaking. Members of Mobilise-D are collaborating to address the critical steps for DMOs to be adopted in clinical trials and ultimately health care. To achieve this, the consortium has developed a roadmap to inform the development, validation and approval of DMOs in Parkinson's disease, multiple sclerosis, chronic obstructive pulmonary disease and recovery from proximal femoral fracture. Here we aim to describe the proposed approach and provide a high-level view of the ongoing and planned work of the Mobilise-D consortium. Ultimately, Mobilise-D aims to stimulate widespread adoption of DMOs through the provision of device agnostic software, standards and robust validation in order to bring digital outcomes from concept to use in clinical trials and health care.
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Affiliation(s)
- Lynn Rochester
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, United Kingdom.,The Newcastle upon Tyne NHS Foundation Trust, Newcastle upon Tyne, United Kingdom
| | - Claudia Mazzà
- Department of Mechanical Engineering, The University of Sheffield, Sheffield, United Kingdom.,INSIGNEO Institute for in Silico Medicine, The University of Sheffield, Sheffield, United Kingdom
| | - Arne Mueller
- Translational Medicine, Novartis Institutes for Biomedical Research, Basel, Switzerland
| | - 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
| | | | - Clemens Becker
- Robert Bosch Foundation for Medical Research, Stuttgart, Germany
| | - Ram Miller
- Translational Medicine, Novartis Institutes for Biomedical Research, Basel, Switzerland
| | - Paolo Piraino
- Research and Early Development Statistics, Bayer, Berlin, Germany
| | | | | | - Judith Garcia-Aymerich
- ISGlobal, Barcelona, Spain.,Universitat Pompeu Fabra (UPF), Barcelona, Spain.,CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Aida A Aydemir
- EMD Serono, Billerica, MA, a Business of Merck KGaA, Darmstadt, Germany
| | - Beatrix Vereijken
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, Trondheim, Norway
| | | | - Nadir Ammour
- Sanofi R&D, Clinical Sciences and Operations, Chilly-Mazarin, France
| | | | - Tilo Hache
- Translational Medicine, Novartis Institutes for Biomedical Research, Basel, Switzerland
| | - Ronenn Roubenoff
- Translational Medicine, Novartis Institutes for Biomedical Research, Basel, Switzerland
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13
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Werner C, Heldmann P, Hummel S, Bauknecht L, Bauer JM, Hauer K. Concurrent Validity, Test-Retest Reliability, and Sensitivity to Change of a Single Body-Fixed Sensor for Gait Analysis during Rollator-Assisted Walking in Acute Geriatric Patients. SENSORS 2020; 20:s20174866. [PMID: 32872168 PMCID: PMC7506931 DOI: 10.3390/s20174866] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/01/2020] [Revised: 08/20/2020] [Accepted: 08/26/2020] [Indexed: 11/16/2022]
Abstract
Body-fixed sensor (BFS) technology offers portable, low-cost and easy-to-use alternatives to laboratory-bound equipment for analyzing an individual's gait. Psychometric properties of single BFS systems for gait analysis in older adults who require a rollator for walking are, however, unknown. The study's aim was to evaluate the concurrent validity, test-retest-reliability, and sensitivity to change of a BFS (DynaPort MoveTest; McRoberts B.V., The Hague, The Netherlands) for measuring gait parameters during rollator-assisted walking. Fifty-eight acutely hospitalized older patients equipped with the BFS at the lower back completed a 10 m walkway using a rollator. Concurrent validity was assessed against the Mobility Lab (APDM Inc.; Portland, OR, USA), test-retest reliability over two trials within a 15 min period, and sensitivity to change in patients with improved, stable and worsened 4 m usual gait speed over hospital stay. Bland-Altman plots and intraclass correlation coefficients (ICC) for gait speed, cadence, step length, step time, and walk ratio indicate good to excellent agreement between the BFS and the Mobility Lab (ICC2,1 = 0.87-0.99) and the repeated trials (ICC2,1 = 0.83-0.92). Moderate to large standardized response means were observed in improved (gait speed, cadence, step length, walk ratio: 0.62-0.99) and worsened patients (gait speed, cadence, step time: -0.52 to -0.85), while those in stable patients were trivial to small (all gait parameters: -0.04-0.40). The BFS appears to be a valid, reliable and sensitive instrument for measuring spatio-temporal gait parameters during rollator-assisted walking in geriatric patients.
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Affiliation(s)
- Christian Werner
- Center for Geriatric Medicine, Heidelberg University, 69117 Heidelberg, Germany;
- AGAPLESION Bethanien Hospital Heidelberg, Geriatric Center at the Heidelberg University, 69126 Heidelberg, Germany;
- Correspondence: ; Tel.: +49-6221-319-1760
| | - Patrick Heldmann
- Network Aging Research (NAR), Heidelberg University, 69117 Heidelberg, Germany;
| | - Saskia Hummel
- Medical Faculty Heidelberg, Heidelberg University, 69117 Heidelberg, Germany; (S.H.); (L.B.)
| | - Laura Bauknecht
- Medical Faculty Heidelberg, Heidelberg University, 69117 Heidelberg, Germany; (S.H.); (L.B.)
| | - Jürgen M. Bauer
- Center for Geriatric Medicine, Heidelberg University, 69117 Heidelberg, Germany;
- AGAPLESION Bethanien Hospital Heidelberg, Geriatric Center at the Heidelberg University, 69126 Heidelberg, Germany;
| | - Klaus Hauer
- AGAPLESION Bethanien Hospital Heidelberg, Geriatric Center at the Heidelberg University, 69126 Heidelberg, Germany;
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14
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Dot T, Quijoux F, Oudre L, Vienne-Jumeau A, Moreau A, Vidal PP, Ricard D. Non-Linear Template-Based Approach for the Study of Locomotion. SENSORS 2020; 20:s20071939. [PMID: 32235667 PMCID: PMC7180476 DOI: 10.3390/s20071939] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/24/2020] [Revised: 03/17/2020] [Accepted: 03/26/2020] [Indexed: 12/25/2022]
Abstract
The automatic detection of gait events (i.e., Initial Contact (IC) and Final Contact (FC)) is crucial for the characterisation of gait from Inertial Measurements Units. In this article, we present a method for detecting steps (i.e., IC and FC) from signals of gait sequences of individuals recorded with a gyrometer. The proposed approach combines the use of a dictionary of templates and a Dynamic Time Warping (DTW) measure of fit to retrieve these templates into input signals. Several strategies for choosing and learning the adequate templates from annotated data are also described. The method is tested on thirteen healthy subjects and compared to gold standard. Depending of the template choice, the proposed algorithm achieves average errors from 0.01 to 0.03 s for the detection of IC, FC and step duration. Results demonstrate that the use of DTW allows achieving these performances with only one single template. DTW is a convenient tool to perform pattern recognition on gait gyrometer signals. This study paves the way for new step detection methods: it shows that using one single template associated with non-linear deformations may be sufficient to model the gait of healthy subjects.
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Affiliation(s)
- Tristan Dot
- Université Paris-Saclay, ENS Paris-Saclay, CNRS, Centre Borelli, F-94235 Cachan, France
- Université de Paris, CNRS, Centre Borelli, F-75005 Paris, France
| | - Flavien Quijoux
- Université Paris-Saclay, ENS Paris-Saclay, CNRS, Centre Borelli, F-94235 Cachan, France
- Université de Paris, CNRS, Centre Borelli, F-75005 Paris, France
- ORPEA Group, F-92813 Puteaux, France
| | - Laurent Oudre
- Université Sorbonne Paris Nord, L2TI, UR 3043, F-93430 Villetaneuse, France
- Correspondence: ; Tel.: +33-1-49-40-40-63
| | - Aliénor Vienne-Jumeau
- Université Paris-Saclay, ENS Paris-Saclay, CNRS, Centre Borelli, F-94235 Cachan, France
- Université de Paris, CNRS, Centre Borelli, F-75005 Paris, France
| | - Albane Moreau
- Université Paris-Saclay, ENS Paris-Saclay, CNRS, Centre Borelli, F-94235 Cachan, France
- Université de Paris, CNRS, Centre Borelli, F-75005 Paris, France
- Service de Neurologie, Service de Santé des Armées, Hôpital d’Instruction des Armées Percy, F-92190 Clamart, France
| | - Pierre-Paul Vidal
- Université Paris-Saclay, ENS Paris-Saclay, CNRS, Centre Borelli, F-94235 Cachan, France
- Université de Paris, CNRS, Centre Borelli, F-75005 Paris, France
- Hangzhou Dianzi University, Hangzhou C-310005, China
| | - Damien Ricard
- Université Paris-Saclay, ENS Paris-Saclay, CNRS, Centre Borelli, F-94235 Cachan, France
- Université de Paris, CNRS, Centre Borelli, F-75005 Paris, France
- Service de Neurologie, Service de Santé des Armées, Hôpital d’Instruction des Armées Percy, F-92190 Clamart, France
- Ecole du Val-de-Grâce, Ecole de Santé des Armées, F-75005 Paris, France
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15
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A Personalized Approach to Improve Walking Detection in Real-Life Settings: Application to Children with Cerebral Palsy. SENSORS 2019; 19:s19235316. [PMID: 31816854 PMCID: PMC6928702 DOI: 10.3390/s19235316] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/31/2019] [Revised: 11/22/2019] [Accepted: 11/29/2019] [Indexed: 12/22/2022]
Abstract
Although many methods have been developed to detect walking by using body-worn inertial sensors, their performances decline when gait patterns become abnormal, as seen in children with cerebral palsy (CP). The aim of this study was to evaluate if fine-tuning an existing walking bouts (WB) detection algorithm by various thresholds, customized at the individual or group level, could improve WB detection in children with CP and typical development (TD). Twenty children (10 CP, 10 TD) wore 4 inertial sensors on their lower limbs during laboratory and out-laboratory assessments. Features extracted from the gyroscope signals recorded in the laboratory were used to tune thresholds of an existing walking detection algorithm for each participant (individual-based personalization: Indiv) or for each group (population-based customization: Pop). Out-of-laboratory recordings were analyzed for WB detection with three versions of the algorithm (i.e., original fixed thresholds and adapted thresholds based on the Indiv and Pop methods), and the results were compared against video reference data. The clinical impact was assessed by quantifying the effect of WB detection error on the estimated walking speed distribution. The two customized Indiv and Pop methods both improved WB detection (higher, sensitivity, accuracy and precision), with the individual-based personalization showing the best results. Comparison of walking speed distribution obtained with the best of the two methods showed a significant difference for 8 out of 20 participants. The personalized Indiv method excluded non-walking activities that were initially wrongly interpreted as extremely slow walking with the initial method using fixed thresholds. Customized methods, particularly individual-based personalization, appear more efficient to detect WB in daily-life settings.
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16
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Bobic VN, Djuric-Jovieic MD, Radovanovic SM, Dragaevic NT, Kostic VS, Popovic MB. Challenges of Stride Segmentation and Their Implementation for Impaired Gait. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2019; 2018:2284-2287. [PMID: 30440862 DOI: 10.1109/embc.2018.8512836] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Stride segmentation represents important but challenging part of the gait analysis. Different methods and sensor systems have been proposed for detection of markers for segmentation of gait sequences. This task is often performed with wearable sensors comprising force sensors and/or inertial sensors. In this paper, we have compared four different methods for stride segmentation based on signals collected from force sensing resistors, accelerometers and gyro sensors. The results were evaluated on 15 healthy and 15 patients with Parkinson's disease, and expressed in terms of number of imprecisely, missed or wrongly detected gait events, as well as temporal absolute error. It was established that the methods using the inertial data, provide results with up to 12% higher error rate comparing to detection from force sensing resistors.
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17
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Ji N, Zhou H, Guo K, Samuel OW, Huang Z, Xu L, Li G. Appropriate Mother Wavelets for Continuous Gait Event Detection Based on Time-Frequency Analysis for Hemiplegic and Healthy Individuals. SENSORS (BASEL, SWITZERLAND) 2019; 19:E3462. [PMID: 31398903 PMCID: PMC6720436 DOI: 10.3390/s19163462] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/20/2019] [Revised: 06/24/2019] [Accepted: 07/02/2019] [Indexed: 11/17/2022]
Abstract
Gait event detection is a crucial step towards the effective assessment and rehabilitation of motor dysfunctions. Recently, the continuous wavelet transform (CWT) based methods have been increasingly proposed for gait event detection due to their robustness. However, few investigations on determining the appropriate mother wavelet with proper selection criteria have been performed, especially for hemiplegic patients. In this study, the performances of commonly used mother wavelets in detecting gait events were systematically investigated. The acceleration signals from the tibialis anterior muscle of both healthy and hemiplegic subjects were recorded during ground walking and the two core gait events of heel strike (HS) and toe off (TO) were detected from the signal recordings by a CWT algorithm with different mother wavelets. Our results showed that the overall performance of the CWT algorithm in detecting the two gait events was significantly different when using various mother wavelets. By using different wavelet selection criteria, we also found that the accuracy criteria based on time-error minimization and F1-score maximization could provide the appropriate mother wavelet for gait event detection. The findings from this study will provide an insight on the selection of an appropriate mother wavelet for gait event detection and facilitate the development of adequate rehabilitation aids.
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Affiliation(s)
- Ning Ji
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110819, China
- CAS Key Lab of Human-Machine Intelligence-Synergy Systems of Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences (CAS), Shenzhen 518055, China
| | - Hui Zhou
- School of Automation, Nanjing University of Science and Technology, Nanjing 210094, China
| | - Kaifeng Guo
- Panyu Central Hospital, Guangzhou 511400, China
| | - Oluwarotimi Williams Samuel
- CAS Key Lab of Human-Machine Intelligence-Synergy Systems of Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences (CAS), Shenzhen 518055, China
| | - Zhen Huang
- Panyu Central Hospital, Guangzhou 511400, China
| | - Lisheng Xu
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110819, China.
| | - Guanglin Li
- CAS Key Lab of Human-Machine Intelligence-Synergy Systems of Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences (CAS), Shenzhen 518055, China.
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18
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Micó-Amigo ME, Kingma I, Heinzel S, Rispens SM, Heger T, Nussbaum S, van Lummel RC, Berg D, Maetzler W, van Dieën JH. Potential Markers of Progression in Idiopathic Parkinson's Disease Derived From Assessment of Circular Gait With a Single Body-Fixed-Sensor: A 5 Year Longitudinal Study. Front Hum Neurosci 2019; 13:59. [PMID: 30837857 PMCID: PMC6389786 DOI: 10.3389/fnhum.2019.00059] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2018] [Accepted: 02/04/2019] [Indexed: 12/03/2022] Open
Abstract
Background and Aim: Development of objective, reliable and easy-to-use methods to obtain progression markers of Parkinson's disease (PD) is required to evaluate interventions and to advance research in PD. This study aimed to provide quantitative markers of progression in idiopathic PD from the assessment of circular gait (walking in circles) with a single body-fixed inertial sensor placed on the lower back. Methods: The assessments were performed every 6 months over a (up to) 5 years period for 22 patients in early-stage PD, 27 patients in middle-stage PD and 25 healthy controls (HC). Longitudinal changes of 24 gait features extracted from accelerometry were compared between PD groups and HCs with generalized estimating equations (GEE) analysis, accounting for gait speed, age and levodopa medication state confounders when required. Results: Five gait features indicated progressive worsening in early stages of PD: number of steps, total duration and harmonic ratios calculated from vertical (VT), medio-lateral (ML), and anterior-posterior (AP) accelerations. For middle stages of PD, three gait features were identified as potential progression markers: stride time variability, and stride regularity from VT and AP acceleration. Conclusion: Faster progressive worsening of gait features in early and middle stages of PD relative to healthy controls over 5 years confirmed the potential of accelerometry-based assessments as quantitative progression markers in early and middle stages of the disease. The difference in significant parameters between both PD groups suggests that distinct domains of gait deteriorate in these PD stages. We conclude that instrumented circular walking assessment is a practical and useful tool in the assessment of PD progression that may have relevant potential to be implemented in clinical trials and even clinical routine, particularly in a developing digital era.
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Affiliation(s)
- M. Encarna Micó-Amigo
- Department of Human Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam Movement Sciences, Amsterdam, Netherlands
| | - Idsart Kingma
- Department of Human Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam Movement Sciences, Amsterdam, Netherlands
| | - Sebastian Heinzel
- Department of Neurology, Christian-Albrechts-University, Kiel, Germany
| | - Sietse M. Rispens
- Department of Human Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam Movement Sciences, Amsterdam, Netherlands
- Personal Health Department, Philips Research Europe, Eindhoven, Netherlands
| | - Tanja Heger
- Department of Neurodegeneration, Center of Neurology, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
- DZNE, German Center for Neurodegenerative Diseases, Tübingen, Germany
| | - Susanne Nussbaum
- Department of Neurodegeneration, Center of Neurology, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
- DZNE, German Center for Neurodegenerative Diseases, Tübingen, Germany
| | | | - Daniela Berg
- Department of Neurology, Christian-Albrechts-University, Kiel, Germany
- Personal Health Department, Philips Research Europe, Eindhoven, Netherlands
- Department of Neurodegeneration, Center of Neurology, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
| | - Walter Maetzler
- Department of Neurology, Christian-Albrechts-University, Kiel, Germany
- Personal Health Department, Philips Research Europe, Eindhoven, Netherlands
- Department of Neurodegeneration, Center of Neurology, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
| | - Jaap H. van Dieën
- Department of Human Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam Movement Sciences, Amsterdam, Netherlands
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19
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Template-Based Step Detection with Inertial Measurement Units. SENSORS 2018; 18:s18114033. [PMID: 30463240 PMCID: PMC6263402 DOI: 10.3390/s18114033] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/07/2018] [Revised: 11/07/2018] [Accepted: 11/16/2018] [Indexed: 11/16/2022]
Abstract
This article presents a method for step detection from accelerometer and gyrometer signals recorded with Inertial Measurement Units (IMUs). The principle of our step detection algorithm is to recognize the start and end times of the steps in the signal thanks to a predefined library of templates. The algorithm is tested on a database of 1020 recordings, composed of healthy subjects and patients with various neurological or orthopedic troubles. Simulations on more than 40,000 steps show that the template-based method achieves remarkable results with a 98% recall and a 98% precision. The method adapts well to pathological subjects and can be used in a medical context for robust step estimation and gait characterization.
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20
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Rucco R, Sorriso A, Liparoti M, Ferraioli G, Sorrentino P, Ambrosanio M, Baselice F. Type and Location of Wearable Sensors for Monitoring Falls during Static and Dynamic Tasks in Healthy Elderly: A Review. SENSORS (BASEL, SWITZERLAND) 2018; 18:E1613. [PMID: 29783647 PMCID: PMC5982638 DOI: 10.3390/s18051613] [Citation(s) in RCA: 76] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/29/2018] [Revised: 04/20/2018] [Accepted: 05/15/2018] [Indexed: 01/28/2023]
Abstract
In recent years, the meaning of successful living has moved from extending lifetime to improving the quality of aging, mainly in terms of high cognitive and physical functioning together with avoiding diseases. In healthy elderly, falls represent an alarming accident both in terms of number of events and the consequent decrease in the quality of life. Stability control is a key approach for studying the genesis of falls, for detecting the event and trying to develop methodologies to prevent it. Wearable sensors have proved to be very useful in monitoring and analyzing the stability of subjects. Within this manuscript, a review of the approaches proposed in the literature for fall risk assessment, fall prevention and fall detection in healthy elderly is provided. The review has been carried out by using the most adopted publication databases and by defining a search strategy based on keywords and boolean algebra constructs. The analysis aims at evaluating the state of the art of such kind of monitoring, both in terms of most adopted sensor technologies and of their location on the human body. The review has been extended to both dynamic and static analyses. In order to provide a useful tool for researchers involved in this field, the manuscript also focuses on the tests conducted in the analyzed studies, mainly in terms of characteristics of the population involved and of the tasks used. Finally, the main trends related to sensor typology, sensor location and tasks have been identified.
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Affiliation(s)
- Rosaria Rucco
- Department of Motor Sciences and Wellness, University of Naples "Parthenope", 80133 Naples, Italy.
- IDC Hermitage Capodimonte, 80133 Naples, Italy.
| | - Antonietta Sorriso
- Department of Engineering, University of Naples "Parthenope", 80133 Naples, Italy.
| | - Marianna Liparoti
- Department of Motor Sciences and Wellness, University of Naples "Parthenope", 80133 Naples, Italy.
- IDC Hermitage Capodimonte, 80133 Naples, Italy.
| | - Giampaolo Ferraioli
- Department of Science and Technologies, University of Naples "Parthenope", 80133 Naples, Italy.
| | - Pierpaolo Sorrentino
- IDC Hermitage Capodimonte, 80133 Naples, Italy.
- Department of Engineering, University of Naples "Parthenope", 80133 Naples, Italy.
| | - Michele Ambrosanio
- Department of Engineering, University of Naples "Parthenope", 80133 Naples, Italy.
| | - Fabio Baselice
- Department of Engineering, University of Naples "Parthenope", 80133 Naples, Italy.
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21
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Pham MH, Elshehabi M, Haertner L, Del Din S, Srulijes K, Heger T, Synofzik M, Hobert MA, Faber GS, Hansen C, Salkovic D, Ferreira JJ, Berg D, Sanchez-Ferro Á, van Dieën JH, Becker C, Rochester L, Schmidt G, Maetzler W. Validation of a Step Detection Algorithm during Straight Walking and Turning in Patients with Parkinson's Disease and Older Adults Using an Inertial Measurement Unit at the Lower Back. Front Neurol 2017; 8:457. [PMID: 28928711 PMCID: PMC5591331 DOI: 10.3389/fneur.2017.00457] [Citation(s) in RCA: 64] [Impact Index Per Article: 9.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2017] [Accepted: 08/17/2017] [Indexed: 11/13/2022] Open
Abstract
INTRODUCTION Inertial measurement units (IMUs) positioned on various body locations allow detailed gait analysis even under unconstrained conditions. From a medical perspective, the assessment of vulnerable populations is of particular relevance, especially in the daily-life environment. Gait analysis algorithms need thorough validation, as many chronic diseases show specific and even unique gait patterns. The aim of this study was therefore to validate an acceleration-based step detection algorithm for patients with Parkinson's disease (PD) and older adults in both a lab-based and home-like environment. METHODS In this prospective observational study, data were captured from a single 6-degrees of freedom IMU (APDM) (3DOF accelerometer and 3DOF gyroscope) worn on the lower back. Detection of heel strike (HS) and toe off (TO) on a treadmill was validated against an optoelectronic system (Vicon) (11 PD patients and 12 older adults). A second independent validation study in the home-like environment was performed against video observation (20 PD patients and 12 older adults) and included step counting during turning and non-turning, defined with a previously published algorithm. RESULTS A continuous wavelet transform (cwt)-based algorithm was developed for step detection with very high agreement with the optoelectronic system. HS detection in PD patients/older adults, respectively, reached 99/99% accuracy. Similar results were obtained for TO (99/100%). In HS detection, Bland-Altman plots showed a mean difference of 0.002 s [95% confidence interval (CI) -0.09 to 0.10] between the algorithm and the optoelectronic system. The Bland-Altman plot for TO detection showed mean differences of 0.00 s (95% CI -0.12 to 0.12). In the home-like assessment, the algorithm for detection of occurrence of steps during turning reached 90% (PD patients)/90% (older adults) sensitivity, 83/88% specificity, and 88/89% accuracy. The detection of steps during non-turning phases reached 91/91% sensitivity, 90/90% specificity, and 91/91% accuracy. CONCLUSION This cwt-based algorithm for step detection measured at the lower back is in high agreement with the optoelectronic system in both PD patients and older adults. This approach and algorithm thus could provide a valuable tool for future research on home-based gait analysis in these vulnerable cohorts.
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Affiliation(s)
- Minh H Pham
- Department of Neurology, University of Kiel, Kiel, Germany.,Digital Signal Processing and System Theory, Faculty of Engineering, University of Kiel, Kiel, Germany
| | - Morad Elshehabi
- Department of Neurology, University of Kiel, Kiel, Germany.,Center for Neurology, Department of Neurodegeneration, Hertie Institute for Clinical Brain Research (HIH), University of Tübingen, Tübingen, Germany
| | - Linda Haertner
- Center for Neurology, Department of Neurodegeneration, Hertie Institute for Clinical Brain Research (HIH), University of Tübingen, Tübingen, Germany.,DZNE, German Center for Neurodegenerative Diseases, Tübingen, Germany
| | - Silvia Del Din
- Institute of Neuroscience/Newcastle University Institute for Ageing, Clinical Ageing Research Unit, Campus for Ageing and Vitality, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Karin Srulijes
- Department of Clinical Gerontology, Robert Bosch Hospital, Stuttgart, Germany
| | - Tanja Heger
- Center for Neurology, Department of Neurodegeneration, Hertie Institute for Clinical Brain Research (HIH), University of Tübingen, Tübingen, Germany.,DZNE, German Center for Neurodegenerative Diseases, Tübingen, Germany
| | - Matthis Synofzik
- Center for Neurology, Department of Neurodegeneration, Hertie Institute for Clinical Brain Research (HIH), University of Tübingen, Tübingen, Germany.,DZNE, German Center for Neurodegenerative Diseases, Tübingen, Germany
| | - Markus A Hobert
- Department of Neurology, University of Kiel, Kiel, Germany.,Center for Neurology, Department of Neurodegeneration, Hertie Institute for Clinical Brain Research (HIH), University of Tübingen, Tübingen, Germany
| | - Gert S Faber
- Department of Human Movement Sciences, MOVE Research Institute Amsterdam, VU University Amsterdam, Amsterdam, Netherlands
| | - Clint Hansen
- Department of Neurology, University of Kiel, Kiel, Germany
| | - Dina Salkovic
- Center for Neurology, Department of Neurodegeneration, Hertie Institute for Clinical Brain Research (HIH), University of Tübingen, Tübingen, Germany
| | - Joaquim J Ferreira
- Clinical Pharmacology Unit, Instituto de Medicina Molecular, Lisbon, Portugal.,Laboratory of Clinical Pharmacology and Therapeutics, Faculty of Medicine, University of Lisbon, Lisbon, Portugal
| | - Daniela Berg
- Department of Neurology, University of Kiel, Kiel, Germany.,Center for Neurology, Department of Neurodegeneration, Hertie Institute for Clinical Brain Research (HIH), University of Tübingen, Tübingen, Germany
| | - Álvaro Sanchez-Ferro
- HM CINAC, Hospital Universitario HM Puerta del Sur, Móstoles, Madrid, Spain.,Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Jaap H van Dieën
- Department of Human Movement Sciences, MOVE Research Institute Amsterdam, VU University Amsterdam, Amsterdam, Netherlands
| | - Clemens Becker
- Department of Clinical Gerontology, Robert Bosch Hospital, Stuttgart, Germany
| | - Lynn Rochester
- Institute of Neuroscience/Newcastle University Institute for Ageing, Clinical Ageing Research Unit, Campus for Ageing and Vitality, Newcastle University, Newcastle upon Tyne, United Kingdom.,Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, United Kingdom
| | - Gerhard Schmidt
- Digital Signal Processing and System Theory, Faculty of Engineering, University of Kiel, Kiel, Germany
| | - Walter Maetzler
- Department of Neurology, University of Kiel, Kiel, Germany.,Center for Neurology, Department of Neurodegeneration, Hertie Institute for Clinical Brain Research (HIH), University of Tübingen, Tübingen, Germany
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22
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Development and validity of methods for the estimation of temporal gait parameters from heel-attached inertial sensors in younger and older adults. Gait Posture 2017; 57:295-298. [PMID: 28686998 DOI: 10.1016/j.gaitpost.2017.06.022] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/06/2017] [Revised: 06/20/2017] [Accepted: 06/22/2017] [Indexed: 02/02/2023]
Abstract
The heel is likely a suitable location to which inertial sensors are attached for the detection of gait events. However, there are few studies to detect gait events and determine temporal gait parameters using sensors attached to the heels. We developed two methods to determine temporal gait parameters: detecting heel-contact using acceleration and detecting toe-off using angular velocity data (acceleration-angular velocity method; A-V method), and detecting both heel-contact and toe-off using angular velocity data (angular velocity-angular velocity method; V-V method). The aim of this study was to examine the concurrent validity of the A-V and V-V methods against the standard method, and to compare their accuracy. Temporal gait parameters were measured in 10 younger and 10 older adults. The intra-class correlation coefficients were excellent in both methods compared with the standard method (0.80 to 1.00). The root mean square errors of stance and swing time in the A-V method were smaller than the V-V method in older adults, although there were no significant discrepancies in the other comparisons. Our study suggests that inertial sensors attached to the heels, using the A-V method in particular, provide a valid measurement of temporal gait parameters.
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23
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Micó-Amigo ME, Kingma I, Faber GS, Kunikoshi A, van Uem JMT, van Lummel RC, Maetzler W, van Dieën JH. Is the Assessment of 5 Meters of Gait with a Single Body-Fixed-Sensor Enough to Recognize Idiopathic Parkinson's Disease-Associated Gait? Ann Biomed Eng 2017; 45:1266-1278. [PMID: 28108943 PMCID: PMC5397518 DOI: 10.1007/s10439-017-1794-8] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2016] [Accepted: 12/29/2016] [Indexed: 11/28/2022]
Abstract
Quantitative assessment of gait in patients with Parkinson’s disease (PD) is an important step in addressing motor symptoms and improving clinical management. Based on the assessment of only 5 meters of gait with a single body-fixed-sensor placed on the lower back, this study presents a method for the identification of step-by-step kinematic parameters in 14 healthy controls and in 28 patients at early-to-moderate stages of idiopathic PD. Differences between groups in step-by-step kinematic parameters were evaluated to understand gait impairments in the PD group. Moreover, a discriminant model between groups was built from a subset of significant and independent parameters and based on a 10-fold cross-validated model. The discriminant model correctly classified a total of 89.5% participants with four kinematic parameters. The sensitivity of the model was 95.8% and the specificity 78.6%. The results indicate that the proposed method permitted to reasonably recognize idiopathic PD-associated gait from 5-m walking assessments. This motivates further investigation on the clinical utility of short episodes of gait assessment with body-fixed-sensors.
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Affiliation(s)
- M E Micó-Amigo
- MOVE Research Institute Amsterdam, Department of Human Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands. .,McRoberts B.V., Raamweg 43, 2596 HN, The Hague, The Netherlands.
| | - I Kingma
- MOVE Research Institute Amsterdam, Department of Human Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - G S Faber
- MOVE Research Institute Amsterdam, Department of Human Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - A Kunikoshi
- McRoberts B.V., Raamweg 43, 2596 HN, The Hague, The Netherlands
| | - J M T van Uem
- Hertie Institute for Clinical Brain Research, Department of Neurodegeneration, Center of Neurology, University of Tübingen, Tübingen, Germany.,DZNE, German Center for Neurodegenerative Diseases, Tübingen, Germany
| | - R C van Lummel
- MOVE Research Institute Amsterdam, Department of Human Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.,McRoberts B.V., Raamweg 43, 2596 HN, The Hague, The Netherlands
| | - W Maetzler
- Hertie Institute for Clinical Brain Research, Department of Neurodegeneration, Center of Neurology, University of Tübingen, Tübingen, Germany.,DZNE, German Center for Neurodegenerative Diseases, Tübingen, Germany
| | - J H van Dieën
- MOVE Research Institute Amsterdam, Department of Human Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
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24
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Maggioni S, Melendez-Calderon A, van Asseldonk E, Klamroth-Marganska V, Lünenburger L, Riener R, van der Kooij H. Robot-aided assessment of lower extremity functions: a review. J Neuroeng Rehabil 2016; 13:72. [PMID: 27485106 PMCID: PMC4969661 DOI: 10.1186/s12984-016-0180-3] [Citation(s) in RCA: 54] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2016] [Accepted: 07/21/2016] [Indexed: 01/01/2023] Open
Abstract
The assessment of sensorimotor functions is extremely important to understand the health status of a patient and its change over time. Assessments are necessary to plan and adjust the therapy in order to maximize the chances of individual recovery. Nowadays, however, assessments are seldom used in clinical practice due to administrative constraints or to inadequate validity, reliability and responsiveness. In clinical trials, more sensitive and reliable measurement scales could unmask changes in physiological variables that would not be visible with existing clinical scores.In the last decades robotic devices have become available for neurorehabilitation training in clinical centers. Besides training, robotic devices can overcome some of the limitations in traditional clinical assessments by providing more objective, sensitive, reliable and time-efficient measurements. However, it is necessary to understand the clinical needs to be able to develop novel robot-aided assessment methods that can be integrated in clinical practice.This paper aims at providing researchers and developers in the field of robotic neurorehabilitation with a comprehensive review of assessment methods for the lower extremities. Among the ICF domains, we included those related to lower extremities sensorimotor functions and walking; for each chapter we present and discuss existing assessments used in routine clinical practice and contrast those to state-of-the-art instrumented and robot-aided technologies. Based on the shortcomings of current assessments, on the identified clinical needs and on the opportunities offered by robotic devices, we propose future directions for research in rehabilitation robotics. The review and recommendations provided in this paper aim to guide the design of the next generation of robot-aided functional assessments, their validation and their translation to clinical practice.
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Affiliation(s)
- Serena Maggioni
- Sensory-Motor Systems (SMS) Lab, Institute of Robotics and Intelligent Systems (IRIS), Department of Health Sciences and Technology (D-HEST), ETH Zürich, Zürich, Switzerland.
- Hocoma AG, Volketswil, Switzerland.
- Spinal Cord Injury Center, Balgrist University Hospital, University Zürich, Zürich, Switzerland.
| | - Alejandro Melendez-Calderon
- Hocoma AG, Volketswil, Switzerland
- Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, IL, USA
| | - Edwin van Asseldonk
- Laboratory of Biomechanical Engineering, MIRA Institute for Biomedical Technology and Technical Medicine, University of Twente, Enschede, The Netherlands
| | - Verena Klamroth-Marganska
- Sensory-Motor Systems (SMS) Lab, Institute of Robotics and Intelligent Systems (IRIS), Department of Health Sciences and Technology (D-HEST), ETH Zürich, Zürich, Switzerland
- Spinal Cord Injury Center, Balgrist University Hospital, University Zürich, Zürich, Switzerland
| | | | - Robert Riener
- Sensory-Motor Systems (SMS) Lab, Institute of Robotics and Intelligent Systems (IRIS), Department of Health Sciences and Technology (D-HEST), ETH Zürich, Zürich, Switzerland
- Spinal Cord Injury Center, Balgrist University Hospital, University Zürich, Zürich, Switzerland
| | - Herman van der Kooij
- Laboratory of Biomechanical Engineering, MIRA Institute for Biomedical Technology and Technical Medicine, University of Twente, Enschede, The Netherlands
- Department of Biomechanical Engineering, Delft University of Technology, Delft, The Netherlands
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