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Fang JR, Pahwa R, Lyons KE, Zanotto T, Sosnoff JJ. Examining the validity of smart glasses in measuring spatiotemporal parameters of gait among people with Parkinson's disease. Gait Posture 2024; 113:139-144. [PMID: 38897002 DOI: 10.1016/j.gaitpost.2024.06.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 01/24/2024] [Accepted: 06/03/2024] [Indexed: 06/21/2024]
Abstract
BACKGROUND Gait impairment is an early marker of Parkinson's disease (PD) and is frequently monitored to evaluate disease progression. Wearable sensors are increasingly being used to quantify gait in the real-world setting among people with PD (pwPD). Particularly, embedding wearables on devices or clothing that are worn daily may represent a useful strategy to improve compliance and regular monitoring of gait. RESEARCH QUESTION The current investigation examined the validity of innovative smart glasses to measure gait among pwPD. METHODS Participants wore the smart glasses and 6 APDM gait sensors simultaneously, while performing two walking tasks: the 3-meters Timed Up and Go test (TUG) and the 7-meters Stand and Walk (SAW) test. The following spatiotemporal gait parameters were calculated from the data collected using the two different devices: step time, step length, swing percentage, TUG duration, turn duration, and turn velocity. RESULTS A total of 31 pwPD (mean age=68.6±8.5 years; 35.48 % female(N=11), mean Unified Parkinson's Disease Rating Scale (UPDRS) total score=32.1±14.7) participated in the study. Smart glasses achieved high validity in measuring step time (ICC=0.92, p=0.01) and TUG duration (ICC=0.96, p=0.03) compared to APDM sensors. On the other hand, the smart glasses did not achieve adequate validity when measuring step length, swing percentage, turn duration or turn velocity. SIGNIFICANCE The current study suggests that smart glasses has the potential to measure TUG and step time in individuals living with PD. However, further research is needed to improve algorithms for sensors worn on the head.
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Affiliation(s)
- James R Fang
- Department of Physical Therapy, Rehabilitation Sciences and Athletic Training, School of Health Professions, University of Kansas Medical Center, Kansas City, KS, United States
| | - Rajesh Pahwa
- Department of Neurology, Parkinson's Disease and Movement Disorder Center, University of Kansas Medical Center, Kansas City, KS, United States
| | - Kelly E Lyons
- Department of Neurology, Parkinson's Disease and Movement Disorder Center, University of Kansas Medical Center, Kansas City, KS, United States
| | - Tobia Zanotto
- Department of Occupational Therapy Education, School of Health Professions, University of Kansas Medical Center, Kansas City, KS, United States; Mobility Core, University of Kansas Center for Community Access, Rehabilitation Research, Education and Service, Kansas City, KS, United States
| | - Jacob J Sosnoff
- Department of Physical Therapy, Rehabilitation Sciences and Athletic Training, School of Health Professions, University of Kansas Medical Center, Kansas City, KS, United States; Department of Occupational Therapy Education, School of Health Professions, University of Kansas Medical Center, Kansas City, KS, United States.
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Rantakokko M, Matikainen-Tervola E, Aartolahti E, Sihvonen S, Chichaeva J, Finni T, Cronin N. Gait Features in Different Environments Contributing to Participation in Outdoor Activities in Old Age (GaitAge): Protocol for an Observational Cross-Sectional Study. JMIR Res Protoc 2024; 13:e52898. [PMID: 38684085 PMCID: PMC11091809 DOI: 10.2196/52898] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Revised: 02/05/2024] [Accepted: 03/22/2024] [Indexed: 05/02/2024] Open
Abstract
BACKGROUND The ability to walk is a key issue for independent old age. Optimizing older peoples' opportunities for an autonomous and active life and reducing health disparities requires a better understanding of how to support independent mobility in older people. With increasing age, changes in gait parameters such as step length and cadence are common and have been shown to increase the risk of mobility decline. However, gait assessments are typically based on laboratory measures, even though walking in a laboratory environment may be significantly different from walking in outdoor environments. OBJECTIVE This project will study alterations in biomechanical features of gait by comparing walking on a treadmill in a laboratory, level outdoor, and hilly outdoor environments. In addition, we will study the possible contribution of changes in gait between these environments to outdoor mobility among older people. METHODS Participants of the study were recruited through senior organizations of Central Finland and the University of the Third Age, Jyväskylä. Inclusion criteria were community-dwelling, aged 70 years and older, able to walk at least 1 km without assistive devices, able to communicate, and living in central Finland. Exclusion criteria were the use of mobility devices, severe sensory deficit (vision and hearing), memory impairment (Mini-Mental State Examination ≤23), and neurological conditions (eg, stroke, Parkinson disease, and multiple sclerosis). The study protocol included 2 research visits. First, indoor measurements were conducted, including interviews (participation, health, and demographics), physical performance tests (short physical performance battery and Timed Up and Go), and motion analysis on a treadmill in the laboratory (3D Vicon and next-generation inertial measurement units [NGIMUs]). Second, outdoor walking tests were conducted, including walking on level (sports track) and hilly (uphill and downhill) terrain, while movement was monitored via NGIMUs, pressure insoles, heart rate, and video data. RESULTS A total of 40 people (n=26, 65% women; mean age 76.3, SD 5.45 years) met the inclusion criteria and took part in the study. Data collection took place between May and September 2022. The first result is expected to be published in the spring of 2024. CONCLUSIONS This multidisciplinary study will provide new scientific knowledge about how gait biomechanics are altered in varied environments, and how this influences opportunities to participate in outdoor activities for older people. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) RR1-10.2196/52898.
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Affiliation(s)
- Merja Rantakokko
- Faculty of Sport and Health Sciences, Gerontology Research Center, University of Jyväskylä, Jyväskylä, Finland
- The Wellbeing Services County of Central Finland, Jyväskylä, Finland
- Institute of Rehabilitation, JAMK University of Applied Sciences, Jyväskylä, Finland
| | | | - Eeva Aartolahti
- Institute of Rehabilitation, JAMK University of Applied Sciences, Jyväskylä, Finland
| | - Sanna Sihvonen
- Institute of Rehabilitation, JAMK University of Applied Sciences, Jyväskylä, Finland
| | - Julija Chichaeva
- Institute of Rehabilitation, JAMK University of Applied Sciences, Jyväskylä, Finland
| | - Taija Finni
- Faculty of Sport and Health Sciences, Neuromuscular Research Centre, University of Jyväskylä, Jyväskylä, Finland
| | - Neil Cronin
- Faculty of Sport and Health Sciences, Neuromuscular Research Centre, University of Jyväskylä, Jyväskylä, Finland
- School of Sport and Exercise, University of Gloucestershire, Gloucester, United Kingdom
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Caparrós-Manosalva C, Espinoza J, Caballero PM, da Cunha MJ, Yang F, Galen S, Pagnussat AS. Movement strategies during obstacle crossing in people with Parkinson disease: A systematic review with meta-analysis. PM R 2024. [PMID: 38656703 DOI: 10.1002/pmrj.13166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Revised: 11/07/2023] [Accepted: 12/01/2023] [Indexed: 04/26/2024]
Abstract
OBJECTIVE Navigating obstacles involves adjusting walking patterns, particularly when stepping over them. This task may be particularly challenging for people with Parkinson disease (PD) for several reasons. This review aims to compare the spatiotemporal gait parameters of people with and without PD while stepping over obstacles. LITERATURE SURVEY A systematic literature search was conducted in six databases (PubMed, Scopus, Web of Science, EBSCO, Embase, and SciELO) from inception to September 2023. METHODOLOGY Studies were selected that evaluated gait parameters of people with and without PD while walking over obstacles. Two independent researchers evaluated the eligibility and extracted gait parameters during obstacle crossing. The risk of bias was assessed using the Joanna Briggs Institute Critical Appraisal Checklist. Heterogeneity was assessed using I2-tests. Random effects models were determined for effect sizes as standardized mean differences (SMD). SYNTHESIS Twenty-five studies were included in the review and 17 in the meta-analysis. Most of the studies (58%) showed a low risk of bias. People with PD exhibit a shorter step when landing after crossing an obstacle (SMD = -0.50 [-0.69 to -0.31]). Compared to people without PD, people with PD also widen their support base (SMD = 0.27 [0.07-0.47]) and reduce gait velocity (SMD = -0.60 [-0.80 to -0.39]) when crossing the obstacle. CONCLUSIONS People with PD adopt a more conservative motor behavior during obstacle crossing than those without PD, with a shorter step length when landing after crossing an obstacle, greater step width and lower crossing speed.
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Affiliation(s)
- Cristian Caparrós-Manosalva
- Department of Human Movement Sciences, Faculty of Health Sciences, University of Talca, Talca, Chile
- Rehabilitation Sciences Graduate Program, Universidade Federal de Ciências da Saúde de Porto Alegre (UFCSPA), Porto Alegre, Brazil
| | - Jessica Espinoza
- Department of Human Movement Sciences, Faculty of Health Sciences, University of Talca, Talca, Chile
- Rehabilitation Sciences Graduate Program, Universidade Federal de Ciências da Saúde de Porto Alegre (UFCSPA), Porto Alegre, Brazil
| | - Paula M Caballero
- Department of Human Movement Sciences, Faculty of Health Sciences, University of Talca, Talca, Chile
| | - Maira J da Cunha
- Rehabilitation Sciences Graduate Program, Universidade Federal de Ciências da Saúde de Porto Alegre (UFCSPA), Porto Alegre, Brazil
- Movement Analysis and Rehabilitation Laboratory, Universidade Federal de Ciências da Saúde de Porto Alegre, UFCSPA, Porto Alegre, Brazil
| | - Feng Yang
- Department of Kinesiology and Health, Georgia State University, Atlanta, Georgia, USA
| | - Sujay Galen
- Department of Physical Therapy, Georgia State University, Atlanta, Georgia, USA
| | - Aline S Pagnussat
- Rehabilitation Sciences Graduate Program, Universidade Federal de Ciências da Saúde de Porto Alegre (UFCSPA), Porto Alegre, Brazil
- Movement Analysis and Rehabilitation Laboratory, Universidade Federal de Ciências da Saúde de Porto Alegre, UFCSPA, Porto Alegre, Brazil
- Department of Physical Therapy, Georgia State University, Atlanta, Georgia, USA
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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] [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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Na CH, Siebers HL, Reim J, Eschweiler J, Hildebrand F, Clusmann H, Betsch M. Kinematic movement and balance parameter analysis in neurological gait disorders. J Biol Eng 2024; 18:6. [PMID: 38225612 PMCID: PMC10790442 DOI: 10.1186/s13036-023-00398-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Accepted: 12/07/2023] [Indexed: 01/17/2024] Open
Abstract
BACKGROUND Neurological gait disorders are mainly classified based on clinical observation, and therefore difficult to objectify or quantify. Movement analysis systems provide objective parameters, which may increase diagnostic accuracy and may aid in monitoring the disease course. Despite the increasing wealth of kinematic movement and balance parameter data, the discriminative value for the differentiation of neurological gait disorders is still unclear. We hypothesized that kinematic motion and balance parameter metrics would be differently altered across neurological gait disorders when compared to healthy controls. METHODS Thirty one patients (9 normal pressure hydrocephalus < NPH > , 16 cervical myelopathy < CM > , 6 lumbar stenosis < LST >) and 14 healthy participants were investigated preoperatively in an outpatient setting using an inertial measurement system (MyoMotion) during 3 different walking tasks (normal walking, dual-task walking with simultaneous backward counting, fast walking). In addition, the natural postural sway of participants was measured by pedobarography, with the eyes opened and closed. The range of motion (ROM) in different joint angles, stride time, as well as sway were compared between different groups (between-subject factor), and different task conditions (within-subject factor) by a mixed model ANOVA. RESULTS Kinematic metrics and balance parameters were differently altered across different gait disorders compared to healthy controls. Overall, NPH patients significantly differed from controls in all movement parameters except for stride time, while they differed in balance parameters only with regard to AP movement. LST patients had significantly reduced ROMs of the shoulders, hips, and ankles, with significantly altered balance parameters regarding AP movement and passed center-of-pressure (COP) distance. CM patients differed from controls only in the ROM of the hip and ankle, but were affected in nearly all balance parameters, except for force distribution. CONCLUSION The application of inertial measurement systems and pedobarography is feasible in an outpatient setting in patients with different neurological gait disorders. Rather than defining singular discriminative values, kinematic gait and balance metrics may provide characteristic profiles of movement parameter alterations in the sense of specific ´gait signatures´ for different pathologies, which could improve diagnostic accuracy by defining objective and quantifiable measures for the discrimination of different neurological gait disorders. TRIAL REGISTRATION The study was retrospectively registered on the 27th of March 2023 in the 'Deutsches Register für Klinische Studien' under the number DRKS00031555.
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Affiliation(s)
- Chuh-Hyoun Na
- Department of Neurosurgery, University Hospital RWTH Aachen, Pauwelsstr. 30, Aachen, 52074, Germany.
| | - Hannah Lena Siebers
- Department of Orthopaedic, Trauma and Reconstructive Surgery, University Hospital RWTH Aachen, Aachen, Germany
| | - Julia Reim
- Department of Orthopaedic, Trauma and Reconstructive Surgery, University Hospital RWTH Aachen, Aachen, Germany
| | - Jörg Eschweiler
- Department of Orthopaedic, Trauma and Reconstructive Surgery, University Hospital RWTH Aachen, Aachen, Germany
| | - Frank Hildebrand
- Department of Orthopaedic, Trauma and Reconstructive Surgery, University Hospital RWTH Aachen, Aachen, Germany
| | - Hans Clusmann
- Department of Neurosurgery, University Hospital RWTH Aachen, Pauwelsstr. 30, Aachen, 52074, Germany
| | - Marcel Betsch
- Department of Trauma and Orthopedic Surgery, University Hospital Erlangen, Friedrich-Alexander-University Erlangen-Nürnberg (FAU), Erlangen, Germany
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Podgoršak A, Flürenbrock F, Trimmel NE, Korn L, Oertel MF, Stieglitz L, Fernandes Dias S, Hierweger MM, Zeilinger M, Weisskopf M, Schmid Daners M. Toward the "Perfect" Shunt: Historical Vignette, Current Efforts, and Future Directions. Adv Tech Stand Neurosurg 2024; 50:1-30. [PMID: 38592526 DOI: 10.1007/978-3-031-53578-9_1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/10/2024]
Abstract
As a concept, drainage of excess fluid volume in the cranium has been around for more than 1000 years. Starting with the original decompression-trepanation of Abulcasis to modern programmable shunt systems, to other nonshunt-based treatments such as endoscopic third ventriculostomy and choroid plexus cauterization, we have come far as a field. However, there are still fundamental limitations that shunts have yet to overcome: namely posture-induced over- and underdrainage, the continual need for valve opening pressure especially in pediatric cases, and the failure to reinstall physiologic intracranial pressure dynamics. However, there are groups worldwide, in the clinic, in industry, and in academia, that are trying to ameliorate the current state of the technology within hydrocephalus treatment. This chapter aims to provide a historical overview of hydrocephalus, current challenges in shunt design, what members of the community have done and continue to do to address these challenges, and finally, a definition of the "perfect" shunt is provided and how the authors are working toward it.
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Affiliation(s)
- Anthony Podgoršak
- Product Development Group Zurich, Department of Mechanical and Process Engineering, ETH Zurich, Zurich, Switzerland
| | - Fabian Flürenbrock
- Institute for Dynamic Systems and Control, Department of Mechanical and Process Engineering, ETH Zurich, Zurich, Switzerland
| | - Nina Eva Trimmel
- Center for Preclinical Development, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Leonie Korn
- Institute for Dynamic Systems and Control, Department of Mechanical and Process Engineering, ETH Zurich, Zurich, Switzerland
| | - Markus Florian Oertel
- Department of Neurosurgery, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Lennart Stieglitz
- Department of Neurosurgery, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Sandra Fernandes Dias
- Department of Neurosurgery, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Melanie Michaela Hierweger
- Center for Preclinical Development, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Melanie Zeilinger
- Institute for Dynamic Systems and Control, Department of Mechanical and Process Engineering, ETH Zurich, Zurich, Switzerland
| | - Miriam Weisskopf
- Center for Preclinical Development, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Marianne Schmid Daners
- Institute for Dynamic Systems and Control, Department of Mechanical and Process Engineering, ETH Zurich, Zurich, Switzerland.
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Ensink C, Smulders K, Warnar J, Keijsers N. Validation of an algorithm to assess regular and irregular gait using inertial sensors in healthy and stroke individuals. PeerJ 2023; 11:e16641. [PMID: 38111664 PMCID: PMC10726747 DOI: 10.7717/peerj.16641] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Accepted: 11/19/2023] [Indexed: 12/20/2023] Open
Abstract
Background Studies using inertial measurement units (IMUs) for gait assessment have shown promising results regarding accuracy of gait event detection and spatiotemporal parameters. However, performance of such algorithms is challenged in irregular walking patterns, such as in individuals with gait deficits. Based on the literature, we developed an algorithm to detect initial contact (IC) and terminal contact (TC) and calculate spatiotemporal gait parameters. We evaluated the validity of this algorithm for regular and irregular gait patterns against a 3D optical motion capture system (OMCS). Methods Twenty healthy participants (aged 59 ± 12 years) and 10 people in the chronic phase after stroke (aged 61 ± 11 years) were equipped with 4 IMUs: on both feet, sternum and lower back (MTw Awinda, Xsens) and 26 reflective makers. Participants walked on an instrumented treadmill for 2 minutes (i) with their preferred stride lengths and (ii) once with irregular stride lengths (±20% deviation) induced by light projected stepping stones. Accuracy of the algorithm was evaluated on stride-by-stride agreement of IC, TC, stride time, length and velocity with OMCS. Bland-Altman-like plots were made for the spatiotemporal parameters, while differences in detection of IC and TC time instances were shown in histogram plots. Performance of the algorithm was compared between regular and irregular gait with a linear mixed model. This was done by comparing the performance in healthy participants in the regular vs irregular walking condition, and by comparing the agreement in healthy participants with stroke participants in the regular walking condition. Results For each condition at least 1,500 strides were included for analysis. Compared to OMCS, IMU-based IC detection in both groups and condition was on average 9-17 (SD ranging from 7 to 35) ms, while IMU-based TC was on average 15-24 (SD ranging from 12 to 35) ms earlier. When comparing regular and irregular gait in healthy participants, the difference between methods was 2.5 ms higher for IC, 3.4 ms lower for TC, 0.3 cm lower for stride length, and 0.4 cm/s higher for stride velocity in the irregular walking condition. No difference was found on stride time. When comparing the differences between methods between healthy and stroke participants, the difference between methods was 7.6 ms lower for IC, 3.8 cm lower for stride length, and 3.4 cm/s lower for stride velocity in stroke participants. No differences were found on differences between methods on TC detection and stride time between stroke and healthy participants. Conclusions Small irrelevant differences were found on gait event detection and spatiotemporal parameters due to irregular walking by imposing irregular stride lengths or pathological (stroke) gait. Furthermore, IMUs seem equally good compared to OMCS to assess gait variability based on stride time, but less accurate based on stride length.
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Affiliation(s)
- Carmen Ensink
- Department of Research, Sint Maartenskliniek, Nijmegen, the Netherlands
- Department of Sensorimotor Neuroscience, Donders institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
| | - Katrijn Smulders
- Department of Research, Sint Maartenskliniek, Nijmegen, the Netherlands
| | - Jolien Warnar
- Department of Research, Sint Maartenskliniek, Nijmegen, the Netherlands
| | - Noel Keijsers
- Department of Research, Sint Maartenskliniek, Nijmegen, the Netherlands
- Department of Sensorimotor Neuroscience, Donders institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
- Department of Rehabilitation, Donders institute for Brain, Cognition and Behaviour, Radboud Univeristy Medical Center, Nijmegen, the Netherlands
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8
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Mitchell C, Cronin J. The variability of dual-task walking parameters using in-shoe inertial sensors in nonconcussed individuals: A randomized within-subject repeated measures design. Health Sci Rep 2023; 6:e1660. [PMID: 37900093 PMCID: PMC10600336 DOI: 10.1002/hsr2.1660] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Revised: 10/08/2023] [Accepted: 10/11/2023] [Indexed: 10/31/2023] Open
Abstract
Background and Aims There is a need for high utility and portability, and cost-effective technologies that are suitable for assessing dual-task gait after experiencing a concussion. Current technologies utilized such as 3D motion capture and force plates are too complex and expensive for most practitioners. The aim of this study was to quantify the variability of dual-task walking gait parameters using in-shoe inertial sensors in nonconcussed individuals. Methods This was a randomized within-subject repeated measures design conducted within a sports laboratory. Twenty healthy, uninjured, nonconcussed participants were recruited for this study. Gait variables of interest were measured across three 2-min continuous walking protocols (12 m, 30 m, 1 min out and back) while performing a cognitive task of counting backward in sevens from a randomly generated number between 300 and 500. Testing was completed over three occasions separated by 7 days, for a total of nine walking trials. Participants completed the testing protocols in a randomized, individual order. The primary outcome was to determine the variability of dual-task walking gait parameters using in-shoe inertial sensors in nonconcussed individuals across three protocols. Results Three to four participants were allocated to each randomized protocol order. Regarding the absolute consistency (coefficient of variation [CV]) between testing occasions, no gait measure was found to have variability above 6.5%. Relative consistency (intraclass correlation coefficient [ICC]) was acceptable (>0.70) in 95% of the variables of interest, with only three variables < 0.70. Similar variability was found across the three testing protocols. Conclusion In-shoe inertial sensors provide a viable option for monitoring gait parameters. This technology is also reliable across different testing distances, thus offering various testing options for practitioners. Further research needs to be conducted to examine the variability with concussed subjects.
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Affiliation(s)
- Courtney Mitchell
- Sport Performance Research in New Zealand (SPRINZ)AUT Millennium Institute, AUT UniversityAucklandNew Zealand
- Department of Sport and RecreationToi Ohomai Institute of TechnologyTaurangaNew Zealand
| | - John Cronin
- Sport Performance Research in New Zealand (SPRINZ)AUT Millennium Institute, AUT UniversityAucklandNew Zealand
- Athlete Training and HealthKatyTexasUSA
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9
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Abdul Jabbar K, Mc Ardle R, Lord S, Kerse N, Del Din S, Teh R. Physical Activity in Community-Dwelling Older Adults: Which Real-World Accelerometry Measures Are Robust? A Systematic Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:7615. [PMID: 37688071 PMCID: PMC10490754 DOI: 10.3390/s23177615] [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: 07/18/2023] [Revised: 08/18/2023] [Accepted: 08/30/2023] [Indexed: 09/10/2023]
Abstract
Measurement of real-world physical activity (PA) data using accelerometry in older adults is informative and clinically relevant, but not without challenges. This review appraises the reliability and validity of accelerometry-based PA measures of older adults collected in real-world conditions. Eight electronic databases were systematically searched, with 13 manuscripts included. Intraclass correlation coefficient (ICC) for inter-rater reliability were: walking duration (0.94 to 0.95), lying duration (0.98 to 0.99), sitting duration (0.78 to 0.99) and standing duration (0.98 to 0.99). ICCs for relative reliability ranged from 0.24 to 0.82 for step counts and 0.48 to 0.86 for active calories. Absolute reliability ranged from 5864 to 10,832 steps and for active calories from 289 to 597 kcal. ICCs for responsiveness for step count were 0.02 to 0.41, and for active calories 0.07 to 0.93. Criterion validity for step count ranged from 0.83 to 0.98. Percentage of agreement for walking ranged from 63.6% to 94.5%; for lying 35.6% to 100%, sitting 79.2% to 100%, and standing 38.6% to 96.1%. Construct validity between step count and criteria for moderate-to-vigorous PA was rs = 0.68 and 0.72. Inter-rater reliability and criterion validity for walking, lying, sitting and standing duration are established. Criterion validity of step count is also established. Clinicians and researchers may use these measures with a limited degree of confidence. Further work is required to establish these properties and to extend the repertoire of PA measures beyond "volume" counts to include more nuanced outcomes such as intensity of movement and duration of postural transitions.
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Affiliation(s)
- Khalid Abdul Jabbar
- School of Population Health, Faculty of Medical and Health Sciences, University of Auckland, Auckland 1023, New Zealand; (K.A.J.); (R.T.)
| | - Ríona Mc Ardle
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne NE2 4HH, UK; (R.M.A.); (S.D.D.)
| | - Sue Lord
- School of Clinical Sciences, Auckland University of Technology, Auckland 1010, New Zealand;
| | - Ngaire Kerse
- School of Population Health, Faculty of Medical and Health Sciences, University of Auckland, Auckland 1023, New Zealand; (K.A.J.); (R.T.)
| | - Silvia Del Din
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne NE2 4HH, UK; (R.M.A.); (S.D.D.)
- The Newcastle upon Tyne Hospitals NHS Foundation Trust, National Institute for Health and Care Research (NIHR), Newcastle Biomedical Research Centre (BRC), Newcastle University, Newcastle upon Tyne NE2 4HH, UK
| | - Ruth Teh
- School of Population Health, Faculty of Medical and Health Sciences, University of Auckland, Auckland 1023, New Zealand; (K.A.J.); (R.T.)
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10
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Zhou L, Fischer E, Brahms CM, Granacher U, Arnrich B. DUO-GAIT: A gait dataset for walking under dual-task and fatigue conditions with inertial measurement units. Sci Data 2023; 10:543. [PMID: 37604913 PMCID: PMC10442385 DOI: 10.1038/s41597-023-02391-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 07/17/2023] [Indexed: 08/23/2023] Open
Abstract
In recent years, there has been a growing interest in developing and evaluating gait analysis algorithms based on inertial measurement unit (IMU) data, which has important implications, including sports, assessment of diseases, and rehabilitation. Multi-tasking and physical fatigue are two relevant aspects of daily life gait monitoring, but there is a lack of publicly available datasets to support the development and testing of methods using a mobile IMU setup. We present a dataset consisting of 6-minute walks under single- (only walking) and dual-task (walking while performing a cognitive task) conditions in unfatigued and fatigued states from sixteen healthy adults. Especially, nine IMUs were placed on the head, chest, lower back, wrists, legs, and feet to record under each of the above-mentioned conditions. The dataset also includes a rich set of spatio-temporal gait parameters that capture the aspects of pace, symmetry, and variability, as well as additional study-related information to support further analysis. This dataset can serve as a foundation for future research on gait monitoring in free-living environments.
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Affiliation(s)
- Lin Zhou
- Digital Health - Connected Healthcare, Hasso Plattner Institute, University of Potsdam, Potsdam, 14482, Germany.
| | - Eric Fischer
- Digital Health - Connected Healthcare, Hasso Plattner Institute, University of Potsdam, Potsdam, 14482, Germany
| | - Clemens Markus Brahms
- Division of Training and Movement Sciences, University of Potsdam, 14469, Potsdam, Germany
- Department of Sport and Sport Science, Exercise and Human Movement Science, University of Freiburg, 79102, Freiburg, Germany
| | - Urs Granacher
- Department of Sport and Sport Science, Exercise and Human Movement Science, University of Freiburg, 79102, Freiburg, Germany
| | - Bert Arnrich
- Digital Health - Connected Healthcare, Hasso Plattner Institute, University of Potsdam, Potsdam, 14482, Germany.
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11
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Charlton JM, Xia H, Shull PB, Eng JJ, Li LC, Hunt MA. Multi-day monitoring of foot progression angles during unsupervised, real-world walking in people with and without knee osteoarthritis. Clin Biomech (Bristol, Avon) 2023; 105:105957. [PMID: 37084548 DOI: 10.1016/j.clinbiomech.2023.105957] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 03/03/2023] [Accepted: 04/13/2023] [Indexed: 04/23/2023]
Abstract
BACKGROUND Foot progression angle is a biomechanical target in gait modification interventions for knee osteoarthritis. To date, it has only been evaluated within laboratory settings. METHODS Adults with symptomatic knee osteoarthritis (n = 30) and healthy adults (n = 15) completed two conditions: 1) treadmill walking in the laboratory (5-min), and 2) real-world walking outside of the laboratory (1-week). Foot progression angle was estimated via shoe-embedded inertial sensing. We calculated the foot progression angle magnitude (median) and variability (interquartile range, coefficient of variation), and used mixed models to compare outcomes between the conditions, participant groups, and disease severities. Reliability was quantified by the intraclass correlation coefficient, standardized error of the measurement, and the minimum detectable change. FINDINGS Foot progression angle magnitude did not differ between groups or conditions but variability significantly higher in real-world walking (P < 0.001). Structural and symptomatic severity were unrelated to FPA in either walking condition, except for real-world coefficient of variation which was higher for moderate-severe structural osteoarthritis compared to the treadmill for those with mild structural severity (P < 0.034). All real-world outcomes showed excellent reliability including intraclass correlation coefficients above 0.95. The participants recorded a mean (standard deviation) of 298 (33) and 10,447 (5232) steps in the laboratory and real-world walking conditions, respectively. INTERPRETATION This study provides the first characterization of foot progression angles during real-world walking in people with and without symptomatic knee osteoarthritis. These results indicate that foot progression angles can be feasibly and reliably measured in unsupervised real-world walking conditions.
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Affiliation(s)
- Jesse M Charlton
- Department of Physical Therapy, University of British Columbia, Vancouver, Canada; Graduate Programs in Rehabilitation Sciences, University of British Columbia, Vancouver, Canada; Motion Analysis and Biofeedback Laboratory, University of British Columbia, Vancouver, Canada; Centre for Aging SMART at Vancouver Coastal Health, Vancouver, Canada.
| | - Haisheng Xia
- Department of Automation, University of Science and Technology of China, Hefei, China; Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, China
| | - Peter B Shull
- State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Janice J Eng
- Department of Physical Therapy, University of British Columbia, Vancouver, Canada; Centre for Aging SMART at Vancouver Coastal Health, Vancouver, Canada
| | - Linda C Li
- Department of Physical Therapy, University of British Columbia, Vancouver, Canada; Arthritis Research Canada, Vancouver, Canada
| | - Michael A Hunt
- Department of Physical Therapy, University of British Columbia, Vancouver, Canada; Motion Analysis and Biofeedback Laboratory, University of British Columbia, Vancouver, Canada; Centre for Aging SMART at Vancouver Coastal Health, Vancouver, Canada
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12
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Dias SF, Graf C, Jehli E, Oertel MF, Mahler J, Schmid Daners M, Stieglitz LH. Gait pattern analysis in the home environment as a key factor for the reliable assessment of shunt responsiveness in patients with idiopathic normal pressure hydrocephalus. Front Neurol 2023; 14:1126298. [PMID: 37082443 PMCID: PMC10110860 DOI: 10.3389/fneur.2023.1126298] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2022] [Accepted: 03/01/2023] [Indexed: 04/07/2023] Open
Abstract
BackgroundThe identification of patients with gait disturbance associated with idiopathic normal pressure hydrocephalus (iNPH) is challenging. This is due to the multifactorial causes of gait disturbance in elderly people and the single moment examination of laboratory tests.ObjectiveWe aimed to assess whether the use of gait sensors in a patient's home environment could help establish a reliable diagnostic tool to identify patients with iNPH by differentiating them from elderly healthy controls (EHC).MethodsFive wearable inertial measurement units were used in 11 patients with iNPH and 20 matched EHCs. Data were collected in the home environment for 72 h. Fifteen spatio-temporal gait parameters were analyzed. Patients were examined preoperatively and postoperatively. We performed an iNPH sub-group analysis to assess differences between responders vs. non-responders. We aimed to identify parameters that are able to predict a reliable response to VP-shunt placement.ResultsNine gait parameters significantly differ between EHC and patients with iNPH preoperatively. Postoperatively, patients with iNPH showed an improvement in the swing phase (p = 0.042), and compared to the EHC group, there was no significant difference regarding the cadence and traveled arm distance. Patients with a good VP-shunt response (NPH recovery rate of ≥5) significantly differ from the non-responders regarding cycle time, cycle time deviation, number of steps, gait velocity, straight length, stance phase, and stance to swing ratio. A receiver operating characteristic analysis showed good sensitivity for a preoperative stride length of ≥0.44 m and gait velocity of ≥0.39 m/s.ConclusionThere was a significant difference in 60% of the analyzed gait parameters between EHC and patients with iNPH, with a clear improvement toward the normalization of the cadence and traveled arm distance postoperatively, and a clear improvement of the swing phase. Patients with iNPH with a good response to VP-shunt significantly differ from the non-responders with an ameliorated gait pattern.
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Affiliation(s)
| | - Christina Graf
- Product Development Group Zurich, Department of Mechanical and Process Engineering, Eidgenoessische Technische Hochschule Zurich, Zurich, Switzerland
| | - Elisabeth Jehli
- Department of Neurosurgery, University Hospital of Zurich, Zurich, Switzerland
- Translational Research Center, University Hospital of Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | | | - Julia Mahler
- Department of Neurosurgery, University Hospital of Zurich, Zurich, Switzerland
| | - Marianne Schmid Daners
- Product Development Group Zurich, Department of Mechanical and Process Engineering, Eidgenoessische Technische Hochschule Zurich, Zurich, Switzerland
- *Correspondence: Marianne Schmid Daners
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13
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Moghadam SM, Yeung T, Choisne J. A comparison of machine learning models' accuracy in predicting lower-limb joints' kinematics, kinetics, and muscle forces from wearable sensors. Sci Rep 2023; 13:5046. [PMID: 36977706 PMCID: PMC10049990 DOI: 10.1038/s41598-023-31906-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Accepted: 03/20/2023] [Indexed: 03/30/2023] Open
Abstract
A combination of wearable sensors' data and Machine Learning (ML) techniques has been used in many studies to predict specific joint angles and moments. The aim of this study was to compare the performance of four different non-linear regression ML models to estimate lower-limb joints' kinematics, kinetics, and muscle forces using Inertial Measurement Units (IMUs) and electromyographys' (EMGs) data. Seventeen healthy volunteers (9F, 28 ± 5 years) were asked to walk over-ground for a minimum of 16 trials. For each trial, marker trajectories and three force-plates data were recorded to calculate pelvis, hip, knee, and ankle kinematics and kinetics, and muscle forces (the targets), as well as 7 IMUs and 16 EMGs. The features from sensors' data were extracted using the Tsfresh python package and fed into 4 ML models; Convolutional Neural Networks (CNN), Random Forest (RF), Support Vector Machine, and Multivariate Adaptive Regression Spline for targets' prediction. The RF and CNN models outperformed the other ML models by providing lower prediction errors in all intended targets with a lower computational cost. This study suggested that a combination of wearable sensors' data with an RF or a CNN model is a promising tool to overcome the limitations of traditional optical motion capture for 3D gait analysis.
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Affiliation(s)
| | - Ted Yeung
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
| | - Julie Choisne
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand.
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14
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Ghaffari A, Rasmussen J, Kold S, Lauritsen REK, Kappel A, Rahbek O. Accelerations Recorded by Simple Inertial Measurement Units with Low Sampling Frequency Can Differentiate between Individuals with and without Knee Osteoarthritis: Implications for Remote Health Care. SENSORS (BASEL, SWITZERLAND) 2023; 23:2734. [PMID: 36904954 PMCID: PMC10006888 DOI: 10.3390/s23052734] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 02/20/2023] [Accepted: 02/28/2023] [Indexed: 06/18/2023]
Abstract
Determining the presence and severity of knee osteoarthritis (OA) is a valuable application of inertial measurement units (IMUs) in the remote monitoring of patients. This study aimed to employ the Fourier representation of IMU signals to differentiate between individuals with and without knee OA. We included 27 patients with unilateral knee osteoarthritis (15 females) and 18 healthy controls (11 females). Gait acceleration signals were recorded during overground walking. We obtained the frequency features of the signals using the Fourier transform. The logistic LASSO regression was employed on the frequency domain features as well as the participant's age, sex, and BMI to distinguish between the acceleration data from individuals with and without knee OA. The model's accuracy was estimated by 10-fold cross-validation. The frequency contents of the signals were different between the two groups. The average accuracy of the classification model using the frequency features was 0.91 ± 0.01. The distribution of the selected features in the final model differed between patients with different severity of knee OA. In this study, we demonstrated that using logistic LASSO regression on the Fourier representation of acceleration signals can accurately determine the presence of knee OA.
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Affiliation(s)
- Arash Ghaffari
- Interdisciplinary Orthopaedics, Aalborg University Hospital, 9000 Aalborg, Denmark
| | - John Rasmussen
- Department of Materials and Production, Aalborg University, 9220 Aalborg East, Denmark
| | - Søren Kold
- Interdisciplinary Orthopaedics, Aalborg University Hospital, 9000 Aalborg, Denmark
| | | | - Andreas Kappel
- Interdisciplinary Orthopaedics, Aalborg University Hospital, 9000 Aalborg, Denmark
| | - Ole Rahbek
- Interdisciplinary Orthopaedics, Aalborg University Hospital, 9000 Aalborg, Denmark
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15
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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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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16
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Fall history in older adults impacts acceleration profiles after a near collision with a moving pedestrian hazard. Aging Clin Exp Res 2023; 35:621-631. [PMID: 36705894 DOI: 10.1007/s40520-023-02345-7] [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: 10/05/2022] [Accepted: 01/11/2023] [Indexed: 01/28/2023]
Abstract
BACKGROUND Environmental hazards (e.g., pedestrian traffic) cause falls and testing environment impacts gait in older adults. However, most fall risk evaluations do not assess real-world moving hazard avoidance. AIMS This study examined the effect of fall history in older adults on acceleration profiles before, during, and after a near collision with a moving hazard, in laboratory and real-world settings. METHODS Older adults with (n = 14) and without a fall history (n = 15) performed a collision avoidance walking task with a sudden moving hazard in real-world and laboratory settings. Gait acceleration and video data of participants' first-person views were recorded. Four mixed effects multilevel models analyzed the magnitude and variability of mean and peak anteroposterior and mediolateral acceleration while walking before, during, and after the moving hazard in both environments. RESULTS In the real-world environment, older adults without a fall history increased their mean anteroposterior acceleration after the moving hazard (p = 0.046), but those with a fall history did not (p > 0.05). Older adults without a fall history exhibited more intersubject variability than those with a fall history in mean (p < 0.001) and peak anteroposterior (p = 0.015) acceleration across environments and epochs. Older adults without a fall history exhibited a slower peak mediolateral reaction during the moving hazard (p = 0.014) than those with a fall history. CONCLUSIONS These results suggest that compared to older adults with a fall history, older adults without a fall history are more adaptable and able to respond last-minute to unexpected hazards. Older adults with a fall history exhibited more homogenous responses.
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Ensink CJ, Smulders K, Warnar JJE, Keijsers NLW. The Influence of Stride Selection on Gait Parameters Collected with Inertial Sensors. SENSORS (BASEL, SWITZERLAND) 2023; 23:2002. [PMID: 36850597 PMCID: PMC9958660 DOI: 10.3390/s23042002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Revised: 02/06/2023] [Accepted: 02/08/2023] [Indexed: 06/18/2023]
Abstract
Different methods exist to select strides that represent preferred, steady-state gait. The aim of this study was to identify the effect of different stride-selection methods on spatiotemporal gait parameters to analyze steady-state gait. A total of 191 patients with hip or knee osteoarthritis (aged 38-85) wearing inertial sensors walked back and forth over 10 m for two minutes. After the removal of strides in turns, five stride-selection methods were compared: (ALL) include all strides, others removed (REFERENCE) two strides around turns, (ONE) one stride around turns, (LENGTH) strides <63% of median stride length, and (SPEED) strides that fall outside the 95% confidence interval of gait speed over the strides included in REFERENCE. Means and SDs of gait parameters were compared for each trial against the most conservative definition (REFERENCE). ONE and SPEED definitions resulted in similar means and SDs compared to REFERENCE, while ALL and LENGTH definitions resulted in substantially higher SDs of all gait parameters. An in-depth analysis of individual strides showed that the first two strides after and last two strides before a turn were significantly different from steady-state walking. Therefore, it is suggested to exclude the first two strides around turns to assess steady-state gait.
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Affiliation(s)
- Carmen J. Ensink
- Department of Research, Sint Maartenskliniek, 6500 GM Nijmegen, The Netherlands
- Department of Sensorimotor Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University, 6500 HB Nijmegen, The Netherlands
| | - Katrijn Smulders
- Department of Research, Sint Maartenskliniek, 6500 GM Nijmegen, The Netherlands
| | - Jolien J. E. Warnar
- Department of Research, Sint Maartenskliniek, 6500 GM Nijmegen, The Netherlands
| | - Noël L. W. Keijsers
- Department of Research, Sint Maartenskliniek, 6500 GM Nijmegen, The Netherlands
- Department of Sensorimotor Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University, 6500 HB Nijmegen, The Netherlands
- Department of Rehabilitation, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, 6500 HB Nijmegen, The Netherlands
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18
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Zhao Q, Chen Z, Landis CD, Lytle A, Rao AK, Zanotto D, Guo Y. Gait monitoring for older adults during guided walking: An integrated assistive robot and wearable sensor approach. WEARABLE TECHNOLOGIES 2022; 3:e28. [PMID: 38486898 PMCID: PMC10936390 DOI: 10.1017/wtc.2022.23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Revised: 07/29/2022] [Accepted: 08/30/2022] [Indexed: 03/17/2024]
Abstract
An active lifestyle can mitigate physical decline and cognitive impairment in older adults. Regular walking exercises for older individuals result in enhanced balance and reduced risk of falling. In this article, we present a study on gait monitoring for older adults during walking using an integrated system encompassing an assistive robot and wearable sensors. The system fuses data from the robot onboard Red Green Blue plus Depth (RGB-D) sensor with inertial and pressure sensors embedded in shoe insoles, and estimates spatiotemporal gait parameters and dynamic margin of stability in real-time. Data collected with 24 participants at a community center reveal associations between gait parameters, physical performance (evaluated with the Short Physical Performance Battery), and cognitive ability (measured with the Montreal Cognitive Assessment). The results validate the feasibility of using such a portable system in out-of-the-lab conditions and will be helpful for designing future technology-enhanced exercise interventions to improve balance, mobility, and strength and potentially reduce falls in older adults.
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Affiliation(s)
- Qingya Zhao
- Department of Mechanical Engineering, Stevens Institute of Technology, Hoboken, NJ, USA
| | - Zhuo Chen
- Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ, USA
| | - Corey D. Landis
- Department of Rehabilitation & Regenerative Medicine (Programs in Physical Therapy G.H. Sergievsky Center), Columbia University, New York, NY, USA
| | - Ashley Lytle
- College of Arts and Letters, Stevens Institute of Technology, Hoboken, NJ, USA
| | - Ashwini K. Rao
- Department of Rehabilitation & Regenerative Medicine (Programs in Physical Therapy G.H. Sergievsky Center), Columbia University, New York, NY, USA
| | - Damiano Zanotto
- Department of Mechanical Engineering, Stevens Institute of Technology, Hoboken, NJ, USA
| | - Yi Guo
- Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ, USA
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Rozanski G, Putrino D. Recording context matters: Differences in gait parameters collected by the OneStep smartphone application. Clin Biomech (Bristol, Avon) 2022; 99:105755. [PMID: 36058106 DOI: 10.1016/j.clinbiomech.2022.105755] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 08/22/2022] [Accepted: 08/25/2022] [Indexed: 02/07/2023]
Abstract
BACKGROUND Detailed understanding of impairments that underlie walking dysfunction through objective measures is essential to diagnosis, evaluation and care planning. Despite significant developments in motion tracking technologies, there is a dearth of research about the influence of remote monitoring context on performance. The objective of this study was to determine whether gait parameters collected by the OneStep smartphone application differ based on the recording condition. METHODS Retrospective repeated measures univariate analysis was performed on data extracted based on detected activity, either spontaneous (background recording) or consciously initiated (in app) walks, of 25 patients enrolled in a physical therapy program. FINDINGS Across 7227 walking bouts, significant differences between the two paradigms in velocity (g = 0.48), double support (g = 0.37), stride length (g = 0.37) and step length of the affected side (g = 0.32) were revealed. Overall, the passively recorded walks presented a less clinically favorable spatiotemporal pattern for each of these variables. INTERPRETATION The recording context of walks that were used for analysis appears to significantly affect the biomechanical output of the OneStep application. It is unclear whether the disparity found would impact functional recovery of individuals undergoing rehabilitation due to neurological or musculoskeletal disorder. Clinicians may consider this information when incorporating remotely-acquired quantitative gait analysis and interpreting care outcomes as part of therapeutic practice. Future work can further investigate the behavioral and environmental factors contributing to how movement occurs in specific clinical populations when monitored via mobile health systems.
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Affiliation(s)
- Gabriela Rozanski
- Abilities Research Center, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America
| | - David Putrino
- Abilities Research Center, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America.
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20
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Pohl J, Ryser A, Veerbeek JM, Verheyden G, Vogt JE, Luft AR, Easthope CA. Accuracy of gait and posture classification using movement sensors in individuals with mobility impairment after stroke. Front Physiol 2022; 13:933987. [PMID: 36225292 PMCID: PMC9549863 DOI: 10.3389/fphys.2022.933987] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2022] [Accepted: 08/29/2022] [Indexed: 11/13/2022] Open
Abstract
Background: Stroke leads to motor impairment which reduces physical activity, negatively affects social participation, and increases the risk of secondary cardiovascular events. Continuous monitoring of physical activity with motion sensors is promising to allow the prescription of tailored treatments in a timely manner. Accurate classification of gait activities and body posture is necessary to extract actionable information for outcome measures from unstructured motion data. We here develop and validate a solution for various sensor configurations specifically for a stroke population.Methods: Video and movement sensor data (locations: wrists, ankles, and chest) were collected from fourteen stroke survivors with motor impairment who performed real-life activities in their home environment. Video data were labeled for five classes of gait and body postures and three classes of transitions that served as ground truth. We trained support vector machine (SVM), logistic regression (LR), and k-nearest neighbor (kNN) models to identify gait bouts only or gait and posture. Model performance was assessed by the nested leave-one-subject-out protocol and compared across five different sensor placement configurations.Results: Our method achieved very good performance when predicting real-life gait versus non-gait (Gait classification) with an accuracy between 85% and 93% across sensor configurations, using SVM and LR modeling. On the much more challenging task of discriminating between the body postures lying, sitting, and standing as well as walking, and stair ascent/descent (Gait and postures classification), our method achieves accuracies between 80% and 86% with at least one ankle and wrist sensor attached unilaterally. The Gait and postures classification performance between SVM and LR was equivalent but superior to kNN.Conclusion: This work presents a comparison of performance when classifying Gait and body postures in post-stroke individuals with different sensor configurations, which provide options for subsequent outcome evaluation. We achieved accurate classification of gait and postures performed in a real-life setting by individuals with a wide range of motor impairments due to stroke. This validated classifier will hopefully prove a useful resource to researchers and clinicians in the increasingly important field of digital health in the form of remote movement monitoring using motion sensors.
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Affiliation(s)
- Johannes Pohl
- Department of Neurology, University of Zurich and University Hospital Zurich, Zurich, Switzerland
- Department of Rehabilitation Sciences, KU Leuven—University of Leuven, Leuven, Belgium
- *Correspondence: Johannes Pohl,
| | - Alain Ryser
- Department of Computer Science, ETH Zurich, Zurich, Switzerland
| | | | - Geert Verheyden
- Department of Rehabilitation Sciences, KU Leuven—University of Leuven, Leuven, Belgium
| | | | - Andreas Rüdiger Luft
- Department of Neurology, University of Zurich and University Hospital Zurich, Zurich, Switzerland
- Cereneo, Center for Neurology and Rehabilitation, Vitznau, Switzerland
| | - Chris Awai Easthope
- Cereneo Foundation, Center for Interdisciplinary Research (CEFIR), Vitznau, Switzerland
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21
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Coviello G, Florio A, Avitabile G, Talarico C, Wang-Roveda JM. Distributed Full Synchronized System for Global Health Monitoring Based on FLSA. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2022; 16:600-608. [PMID: 35536796 DOI: 10.1109/tbcas.2022.3173586] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
In modern medicine, smart wireless connected devices are gaining an increasingly important role in aiding doctors' job of monitoring patients. More and more complex systems, with a high density of sensors capable of monitoring many biological signals, are arising. Merging the data offers a great opportunity for increasing the reliability of diagnosis. However, a huge problem is constituted by synchronization. Multi-board wireless-connected monitoring systems are a typical example of distributed systems and synchronization has always been a challenging issue. In this paper, we present a distributed full synchronized system for monitoring patients' health capable of heartbeat rate, oxygen saturation, gait and posture analysis, and muscle activity measurements. The time synchronization is guaranteed thanks to the Fractional Low-power Synchronization Algorithm (FLSA).
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22
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A Comparison of Turning Kinematics at Different Amplitudes during Standing Turns between Older and Younger Adults. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12115474] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
It is well-established that processes involving changing direction or turning in which either or both standing and walking turns are utilized involve coordination of the whole-body and stepping characteristics. However, the turn context and whole-body coordination have not been fully explored during different turning amplitudes. For these reasons, this present study aimed to determine the effects of turning amplitude on whole-body coordination. The findings from this study can be utilized to inform the rationale behind fall prevention factors and to help design an exercise strategy to address issues related to amplitude of turning in older adults. Twenty healthy older and twenty healthy younger adults were asked to complete standing turns on level ground using three randomly selected amplitudes, 90°, 135° and 180°, at their self-selected turn speed. Turning kinematics and stepping variables were recorded using Inertial Measurement Units. Analysis of the data was carried out using Mixed Model Analysis of Variance with two factors (2 groups × 3 turning amplitudes) and further post hoc pairwise analysis to examine differences between factors. There were significant interaction effects (p < 0.05) between the groups and turning amplitudes for step duration and turn speed. Further analysis using Repeated Measure Analysis of Variance tests determined a main effect of amplitude on step duration and turn speed within each group. Furthermore, post hoc pairwise comparisons revealed that the step duration and turn speed increased significantly (p < 0.001) with all increases in turning amplitude in both groups. In addition, significant main effects for group and amplitudes were seen for onset latency of movement for the head, thorax, pelvis, and feet, and for peak head–thorax and peak head–pelvis angular separations and stepping characteristics, which all increased with turn amplitude and showed differences between groups. These results suggest that large amplitude turns result in a change in turning and stepping kinematics. Therefore, when assessing the turning characteristics of older adults or those in frail populations, the turning amplitude should be taken into account during turning, and could be gradually increased to challenge motor control as part of exercise falls prevention strategies.
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Weston AR, Loyd BJ, Taylor C, Hoppes C, Dibble LE. Head and Trunk Kinematics during Activities of Daily Living with and without Mechanical Restriction of Cervical Motion. SENSORS (BASEL, SWITZERLAND) 2022; 22:3071. [PMID: 35459056 PMCID: PMC9026113 DOI: 10.3390/s22083071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Revised: 04/06/2022] [Accepted: 04/12/2022] [Indexed: 11/16/2022]
Abstract
Alterations in head and trunk kinematics during activities of daily living can be difficult to recognize and quantify with visual observation. Incorporating wearable sensors allows for accurate and measurable assessment of movement. The aim of this study was to determine the ability of wearable sensors and data processing algorithms to discern motion restrictions during activities of daily living. Accelerometer data was collected with wearable sensors from 10 healthy adults (age 39.5 ± 12.47) as they performed daily living simulated tasks: coin pick up (pitch plane task), don/doff jacket (yaw plane task), self-paced community ambulation task [CAT] (pitch and yaw plane task) without and with a rigid cervical collar. Paired t-tests were used to discern differences between non-restricted (no collared) performance and restricted (collared) performance of tasks. Significant differences in head rotational velocity (jacket p = 0.03, CAT-pitch p < 0.001, CAT-yaw p < 0.001), head rotational amplitude (coin p = 0.03, CAT-pitch p < 0.001, CAT-yaw p < 0.001), trunk rotational amplitude (jacket p = 0.01, CAT-yaw p = 0.005), and head−trunk coupling (jacket p = 0.007, CAT-yaw p = 0.003) were captured by wearable sensors between the two conditions. Alterations in turning movement were detected at the head and trunk during daily living tasks. These results support the ecological validity of using wearable sensors to quantify movement alterations during real-world scenarios.
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Affiliation(s)
- Angela R. Weston
- Department of Physical Therapy and Athletic Training, University of Utah, 520 Wakara Way, Salt Lake City, UT 84108, USA;
| | - Brian J. Loyd
- Department of Physical Therapy and Rehabilitation Sciences, University of Montana, 32 Campus Dr., Missoula, MT 59812, USA;
| | - Carolyn Taylor
- Department of Orthopedics, University of Utah, 590 Wakara Way, Salt Lake City, UT 84108, USA;
| | - Carrie Hoppes
- Army Baylor University Doctoral Program in Physical Therapy, U.S. Army Medical Center of Excellence, 3630 Stanley Road, San Antonio, TX 78234, USA;
| | - Leland E. Dibble
- Department of Physical Therapy and Athletic Training, University of Utah, 520 Wakara Way, Salt Lake City, UT 84108, USA;
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Wearable Sensors for Vital Signs Measurement: A Survey. JOURNAL OF SENSOR AND ACTUATOR NETWORKS 2022. [DOI: 10.3390/jsan11010019] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
With the outbreak of coronavirus disease-2019 (COVID-19) worldwide, developments in the medical field have aroused concerns within society. As science and technology develop, wearable medical sensors have become the main means of medical data acquisition. To analyze the intelligent development status of wearable medical sensors, the current work classifies and prospects the application status and functions of wireless communication wearable medical sensors, based on human physiological data acquisition in the medical field. By understanding its working principles, data acquisition modes and action modes, the work chiefly analyzes the application of wearable medical sensors in vascular infarction, respiratory intensity, body temperature, blood oxygen concentration, and sleep detection, and reflects the key role of wearable medical sensors in human physiological data acquisition. Further exploration and prospecting are made by investigating the improvement of information security performance of wearable medical sensors, the improvement of biological adaptability and biodegradability of new materials, and the integration of wearable medical sensors and intelligence-assisted rehabilitation. The research expects to provide a reference for the intelligent development of wearable medical sensors and real-time monitoring of human health in the follow-up medical field.
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Simple rule to automatically recognize the orientation of the sagittal plane foot angular velocity for gait analysis using IMUs on the feet of individuals with heterogeneous motor disabilities. J Biomech 2022; 135:111055. [DOI: 10.1016/j.jbiomech.2022.111055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Revised: 02/28/2022] [Accepted: 03/15/2022] [Indexed: 11/18/2022]
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26
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Comparison between Helical Axis and SARA Approaches for the Estimation of Functional Joint Axes on Multi-Body Modeling Data. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12031274] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/04/2022]
Abstract
Functional methods usually allow for a flexible and accurate representation of joint kinematics and are increasingly implemented both for clinical and biomechanics research purposes. This paper presents a quantitative comparison between two widely adopted methods for functional axis estimation, that is, the helical axis theory and the symmetrical axis of rotation approach (SARA). To this purpose, a multi-body model was developed to simulate the lower limb of a subject. This model was designed to reproduce different motion patterns, that is, by selecting the active degrees of freedom of the simulated ankle joint. Thanks to virtual markers attached to each segment, the multi-body model was used to generate simulated motion capture data that were then analyzed by instantaneous helical axes and SARA algorithms. To achieve a synthetic representation of joint kinematics, a mean helical axis and an average SARA functional axis were estimated, along with dispersion parameters and rms distance data that were used to quantitatively assess the performance of each method. The sensitivity of each algorithm to different combinations of range and speed of motion, scattering of marker clusters, sampling rate, and additive noise on markers’ trajectories, was finally evaluated.
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27
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Betteridge C, Mobbs RJ, Fonseka RD, Natarajan P, Ho D, Choy WJ, Sy LW, Pell N. Objectifying clinical gait assessment: using a single-point wearable sensor to quantify the spatiotemporal gait metrics of people with lumbar spinal stenosis. JOURNAL OF SPINE SURGERY 2021; 7:254-268. [PMID: 34734130 DOI: 10.21037/jss-21-16] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/13/2021] [Accepted: 06/25/2021] [Indexed: 11/06/2022]
Abstract
Background Wearable accelerometer-containing devices have become a mainstay in clinical studies which attempt to classify the gait patterns in various diseases. A gait profile for lumbar spinal stenosis (LSS) has not been developed, and no study has validated a simple wearable system for the clinical assessment of gait in lumbar stenosis. This study identifies the changes to gait patterns that occur in LSS to create a preliminary disease-specific gait profile. In addition, this study compares a chest-based wearable sensor, the MetaMotionC© device and inertial measurement unit python script (MMC/IMUPY) system, against a reference-standard, videography, to preliminarily assess its accuracy in measuring the gait features of patients with LSS. Methods We conduct a cross-sectional observational study examining the walking patterns of 25 LSS patients and 33 healthy controls. To construct a preliminary disease-specific gait profile for LSS, the gait patterns of the 25 LSS patients and 25 healthy controls with similar ages were compared. To assess the accuracy of the MMC/IMUPY system in measuring the gait features of patients with LSS, its results were compared with videography for the 21 LSS and 33 healthy controls whose walking bouts exceeded 30 m. Results Patients suffering from LSS walked significantly slower, with shorter, less frequent steps and higher asymmetry compared to healthy controls. The MMC/IMUPY system had >90% agreement with videography for all spatiotemporal gait metrics that both methods could measure. Conclusions The MMC/IMUPY system is a simple and feasible system for the construction of a preliminary disease-specific gait profile for LSS. Before clinical application in everyday living conditions is possible, further studies involving the construction of a more detailed disease-specific gait profile for LSS by disease severity, and the validation of the MMC/IMUPY system in the home environment, are required.
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Affiliation(s)
- Callum Betteridge
- Faculty of Medicine, University of New South Wales, Sydney, Australia.,NeuroSpine Surgery Research Group, Sydney, Australia.,NeuroSpine Clinic, Prince of Wales Private Hospital, Randwick, Australia.,Wearables and Gait Assessment Group, Sydney, Australia
| | - Ralph J Mobbs
- Faculty of Medicine, University of New South Wales, Sydney, Australia.,NeuroSpine Surgery Research Group, Sydney, Australia.,NeuroSpine Clinic, Prince of Wales Private Hospital, Randwick, Australia.,Wearables and Gait Assessment Group, Sydney, Australia
| | - R Dineth Fonseka
- Faculty of Medicine, University of New South Wales, Sydney, Australia.,NeuroSpine Surgery Research Group, Sydney, Australia.,NeuroSpine Clinic, Prince of Wales Private Hospital, Randwick, Australia.,Wearables and Gait Assessment Group, Sydney, Australia
| | - Pragadesh Natarajan
- Faculty of Medicine, University of New South Wales, Sydney, Australia.,NeuroSpine Surgery Research Group, Sydney, Australia.,NeuroSpine Clinic, Prince of Wales Private Hospital, Randwick, Australia.,Wearables and Gait Assessment Group, Sydney, Australia
| | - Daniel Ho
- Faculty of Medicine, University of New South Wales, Sydney, Australia.,NeuroSpine Surgery Research Group, Sydney, Australia.,NeuroSpine Clinic, Prince of Wales Private Hospital, Randwick, Australia.,Wearables and Gait Assessment Group, Sydney, Australia
| | - Wen Jie Choy
- Faculty of Medicine, University of New South Wales, Sydney, Australia.,NeuroSpine Surgery Research Group, Sydney, Australia.,NeuroSpine Clinic, Prince of Wales Private Hospital, Randwick, Australia.,Wearables and Gait Assessment Group, Sydney, Australia
| | - Luke W Sy
- NeuroSpine Surgery Research Group, Sydney, Australia.,NeuroSpine Clinic, Prince of Wales Private Hospital, Randwick, Australia.,Wearables and Gait Assessment Group, Sydney, Australia.,School of Biomechanics, University of New South Wales, Sydney, Australia
| | - Nina Pell
- NeuroSpine Surgery Research Group, Sydney, Australia.,NeuroSpine Clinic, Prince of Wales Private Hospital, Randwick, Australia.,Wearables and Gait Assessment Group, Sydney, Australia
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Falls in Post-Polio Patients: Prevalence and Risk Factors. BIOLOGY 2021; 10:biology10111110. [PMID: 34827103 PMCID: PMC8614826 DOI: 10.3390/biology10111110] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Revised: 10/26/2021] [Accepted: 10/27/2021] [Indexed: 11/16/2022]
Abstract
Simple Summary People with post-polio syndrome (PPS) suffer frequent falls due to muscle weakness and problems with their balance. In order for a rehabilitation clinician to fit the patient with the optimal treatment plan to prevent imbalance and falls, we performed a simple 10-min walking test with 50 PPS patients. We also asked the patients how many falls they had experienced in the last year and they filled out a questionnaire regarding their balance confidence. We found that we can predict the occurrence of falls in PPS patients based on the consistency of their walking pattern. Since it is very easy to measure the walking pattern, our results may help rehabilitation clinicians to identify individuals at risk of fall and reduce the occurrence of falls in this population. Abstract Individuals with post-polio syndrome (PPS) suffer from falls and secondary damage. Aim: To (i) analyze the correlation between spatio-temporal gait data and fall measures (fear and frequency of falls) and to (ii) test whether the gait parameters are predictors of fall measures in PPS patients. Methods: Spatio-temporal gait data of 50 individuals with PPS (25 males; age 65.9 ± 8.0) were acquired during gait and while performing the Timed Up-and-Go test. Subjects filled the Activities-specific Balance Confidence Scale (ABC Scale) and reported number of falls during the past year. Results: ABC scores and number of falls correlated with the Timed Up-and-Go, and gait cadence and velocity. The number of falls also correlated with the swing duration symmetry index and the step length variability. Four gait variability parameters explained 33.2% of the variance of the report of falls (p = 0.006). The gait velocity was the best predictor of the ABC score and explained 24.8% of its variance (p = 0.001). Conclusion: Gait variability, easily measured by wearables or pressure-sensing mats, is an important predictor of falls in PPS population. Therefore, gait variability might be an efficient tool before devising a patient-specific fall prevention program for the PPS patient.
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Kuruvithadam K, Menner M, Taylor WR, Zeilinger MN, Stieglitz L, Schmid Daners M. Data-Driven Investigation of Gait Patterns in Individuals Affected by Normal Pressure Hydrocephalus. SENSORS 2021; 21:s21196451. [PMID: 34640771 PMCID: PMC8512819 DOI: 10.3390/s21196451] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Revised: 09/14/2021] [Accepted: 09/15/2021] [Indexed: 11/16/2022]
Abstract
Normal pressure hydrocephalus (NPH) is a chronic and progressive disease that affects predominantly elderly subjects. The most prevalent symptoms are gait disorders, generally determined by visual observation or measurements taken in complex laboratory environments. However, controlled testing environments can have a significant influence on the way subjects walk and hinder the identification of natural walking characteristics. The study aimed to investigate the differences in walking patterns between a controlled environment (10 m walking test) and real-world environment (72 h recording) based on measurements taken via a wearable gait assessment device. We tested whether real-world environment measurements can be beneficial for the identification of gait disorders by performing a comparison of patients’ gait parameters with an aged-matched control group in both environments. Subsequently, we implemented four machine learning classifiers to inspect the individual strides’ profiles. Our results on twenty young subjects, twenty elderly subjects and twelve NPH patients indicate that patients exhibited a considerable difference between the two environments, in particular gait speed (p-value p=0.0073), stride length (p-value p=0.0073), foot clearance (p-value p=0.0117) and swing/stance ratio (p-value p=0.0098). Importantly, measurements taken in real-world environments yield a better discrimination of NPH patients compared to the controlled setting. Finally, the use of stride classifiers provides promise in the identification of strides affected by motion disorders.
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Affiliation(s)
- Kiran Kuruvithadam
- Product Development Group Zurich, Department of Mechanical and Process Engineering, ETH Zurich, 8092 Zurich, Switzerland;
| | - Marcel Menner
- Institute for Dynamic Systems and Control, ETH Zurich, 8092 Zurich, Switzerland; (M.M.); (M.N.Z.)
| | - William R. Taylor
- Laboratory for Movement Biomechanics, Institute for Biomechanics, ETH Zurich, 8093 Zurich, Switzerland;
| | - Melanie N. Zeilinger
- Institute for Dynamic Systems and Control, ETH Zurich, 8092 Zurich, Switzerland; (M.M.); (M.N.Z.)
| | - Lennart Stieglitz
- Department of Neurosurgery, University Hospital Zurich, 8091 Zurich, Switzerland;
| | - Marianne Schmid Daners
- Product Development Group Zurich, Department of Mechanical and Process Engineering, ETH Zurich, 8092 Zurich, Switzerland;
- Correspondence:
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Luna A, Casertano L, Timmerberg J, O'Neil M, Machowsky J, Leu CS, Lin J, Fang Z, Douglas W, Agrawal S. Artificial intelligence application versus physical therapist for squat evaluation: a randomized controlled trial. Sci Rep 2021; 11:18109. [PMID: 34518568 PMCID: PMC8437936 DOI: 10.1038/s41598-021-97343-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Accepted: 08/18/2021] [Indexed: 11/24/2022] Open
Abstract
Artificial intelligence technology is becoming more prevalent in health care as a tool to improve practice patterns and patient outcomes. This study assessed ability of a commercialized artificial intelligence (AI) mobile application to identify and improve bodyweight squat form in adult participants when compared to a physical therapist (PT). Participants randomized to AI group (n = 15) performed 3 squat sets: 10 unassisted control squats, 10 squats with performance feedback from AI, and 10 additional unassisted test squats. Participants randomized to PT group (n = 15) also performed 3 identical sets, but instead received performance feedback from PT. AI group intervention did not differ from PT group (log ratio of two odds ratios = − 0.462, 95% confidence interval (CI) (− 1.394, 0.471), p = 0.332). AI ability to identify a correct squat generated sensitivity 0.840 (95% CI (0.753, 0.901)), specificity 0.276 (95% CI (0.191, 0.382)), PPV 0.549 (95% CI (0.423, 0.669)), NPV 0.623 (95% CI (0.436, 0.780)), and accuracy 0.565 95% CI (0.477, 0.649)). There was no statistically significant association between group allocation and improved squat performance. Current AI had satisfactory ability to identify correct squat form and limited ability to identify incorrect squat form, which reduced diagnostic capabilities. Trial Registration NCT04624594, 12/11/2020, retrospectively registered.
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Affiliation(s)
- Alessandro Luna
- Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, USA
| | - Lorenzo Casertano
- NewYork-Presbyterian/Columbia University Irving Medical Center, New York, NY, USA
| | - Jean Timmerberg
- Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, USA.,Columbia University Irving Medical Center, New York, NY, USA
| | - Margaret O'Neil
- Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, USA
| | | | - Cheng-Shiun Leu
- Department of Biostatistics, Columbia University, New York, NY, USA
| | - Jianghui Lin
- Department of Biostatistics, Columbia University, New York, NY, USA
| | - Zhiqian Fang
- Department of Biostatistics, Columbia University, New York, NY, USA.,Department of Psychiatry, University of Hong Kong, Pok Fu Lam, Hong Kong
| | - William Douglas
- NewYork-Presbyterian/Columbia University Irving Medical Center, New York, NY, USA
| | - Sunil Agrawal
- Departments of Mechanical Engineering and Rehabilitation Medicine, Columbia University, 500 W. 120th Street #510, New York, NY, 10027, USA.
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Automated Loss-of-Balance Event Identification in Older Adults at Risk of Falls during Real-World Walking Using Wearable Inertial Measurement Units. SENSORS 2021; 21:s21144661. [PMID: 34300399 PMCID: PMC8309544 DOI: 10.3390/s21144661] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Revised: 06/18/2021] [Accepted: 06/28/2021] [Indexed: 02/07/2023]
Abstract
Loss-of-balance (LOB) events, such as trips and slips, are frequent among community-dwelling older adults and are an indicator of increased fall risk. In a preliminary study, eight community-dwelling older adults with a history of falls were asked to perform everyday tasks in the real world while donning a set of three inertial measurement sensors (IMUs) and report LOB events via a voice-recording device. Over 290 h of real-world kinematic data were collected and used to build and evaluate classification models to detect the occurrence of LOB events. Spatiotemporal gait metrics were calculated, and time stamps for when LOB events occurred were identified. Using these data and machine learning approaches, we built classifiers to detect LOB events. Through a leave-one-participant-out validation scheme, performance was assessed in terms of the area under the receiver operating characteristic curve (AUROC) and the area under the precision recall curve (AUPR). The best model achieved an AUROC ≥0.87 for every held-out participant and an AUPR 4-20 times the incidence rate of LOB events. Such models could be used to filter large datasets prior to manual classification by a trained healthcare provider. In this context, the models filtered out at least 65.7% of the data, while detecting ≥87.0% of events on average. Based on the demonstrated discriminative ability to separate LOBs and normal walking segments, such models could be applied retrospectively to track the occurrence of LOBs over an extended period of time.
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The Effect of Different Turn Speeds on Whole-Body Coordination in Younger and Older Healthy Adults. SENSORS 2021; 21:s21082827. [PMID: 33923838 PMCID: PMC8074235 DOI: 10.3390/s21082827] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Revised: 04/03/2021] [Accepted: 04/14/2021] [Indexed: 01/12/2023]
Abstract
Difficulty in turning is prevalent in older adults and results in postural instability and risk of falling. Despite this, the mechanisms of turning problems have yet to be fully determined, and it is unclear if different speeds directly result in altered posture and turning characteristics. The aim of this study was to identify the effects of turning speeds on whole-body coordination and to explore if these can be used to help inform fall prevention programs in older adults. Forty-two participants (21 healthy older adults and 21 younger adults) completed standing turns on level ground. Inertial Measurement Units (XSENS) were used to measure turning kinematics and stepping characteristics. Participants were randomly tasked to turn 180° at one of three speeds; fast, moderate, or slow to the left and right. Two factors mixed model analysis of variance (MM ANOVA) with post hoc pairwise comparisons were performed to assess the two groups and three turning speeds. Significant interaction effects (p < 0.05) were seen in; reorientation onset latency of head, pelvis, and feet, peak segmental angular separation, and stepping characteristics (step frequency and step size), which all changed with increasing turn speed. Repeated measures ANOVA revealed the main effects of speeds within the older adults group on those variables as well as the younger adults group. Our results suggest that turning speeds result in altered whole-body coordination and stepping behavior in older adults, which use the same temporospatial sequence as younger adults. However, some characteristics differ significantly, e.g., onset latency of segments, peak head velocity, step frequency, and step size. Therefore, the assessment of turning speeds elucidates the exact temporospatial differences between older and younger healthy adults and may help to determine some of the issues that the older population face during turning, and ultimately the altered whole-body coordination, which lead to falls.
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Sczuka KS, Schneider M, Bourke AK, Mellone S, Kerse N, Helbostad JL, Becker C, Klenk J. Template-Based Recognition of Human Locomotion in IMU Sensor Data Using Dynamic Time Warping. SENSORS 2021; 21:s21082601. [PMID: 33917260 PMCID: PMC8067979 DOI: 10.3390/s21082601] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Revised: 03/16/2021] [Accepted: 03/29/2021] [Indexed: 11/19/2022]
Abstract
Increased levels of light, moderate and vigorous physical activity (PA) are positively associated with health benefits. Therefore, sensor-based human activity recognition can identify different types and levels of PA. In this paper, we propose a two-layer locomotion recognition method using dynamic time warping applied to inertial sensor data. Based on a video-validated dataset (ADAPT), which included inertial sensor data recorded at the lower back (L5 position) during an unsupervised task-based free-living protocol, the recognition algorithm was developed, validated and tested. As a first step, we focused on the identification of locomotion activities walking, ascending and descending stairs. These activities are difficult to differentiate due to a high similarity. The results showed that walking could be recognized with a sensitivity of 88% and a specificity of 89%. Specificity for stair climbing was higher compared to walking, but sensitivity was noticeably decreased. In most cases of misclassification, stair climbing was falsely detected as walking, with only 0.2–5% not assigned to any of the chosen types of locomotion. Our results demonstrate a promising approach to recognize and differentiate human locomotion within a variety of daily activities.
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Affiliation(s)
- Kim S. Sczuka
- Department of Clinical Gerontology, Robert-Bosch-Hospital, Auerbachstr. 110, 70376 Stuttgart, Germany; (M.S.); (C.B.); (J.K.)
- Correspondence: ; Tel.: +49-711-8101-6078
| | - Marc Schneider
- Department of Clinical Gerontology, Robert-Bosch-Hospital, Auerbachstr. 110, 70376 Stuttgart, Germany; (M.S.); (C.B.); (J.K.)
| | - Alan K. Bourke
- Department of Neuroscience, NTNU, 7491 Trondheim, Norway; (A.K.B.); (J.L.H.)
| | - Sabato Mellone
- Department of Electrical, Electronic, and Information Engineering, University of Bologna, 40136 Bologna, Italy;
| | - Ngaire Kerse
- Department of General Practice and Primary Health Care, University of Auckland, Auckland 1023, New Zealand;
| | - Jorunn L. Helbostad
- Department of Neuroscience, NTNU, 7491 Trondheim, Norway; (A.K.B.); (J.L.H.)
| | - Clemens Becker
- Department of Clinical Gerontology, Robert-Bosch-Hospital, Auerbachstr. 110, 70376 Stuttgart, Germany; (M.S.); (C.B.); (J.K.)
| | - Jochen Klenk
- Department of Clinical Gerontology, Robert-Bosch-Hospital, Auerbachstr. 110, 70376 Stuttgart, Germany; (M.S.); (C.B.); (J.K.)
- Institute of Epidemiology and Medical Biometry, Ulm University, Helmholtzstr. 22, 89081 Ulm, Germany
- Study Center Stuttgart, IB University for Health and Social Sciences, Paulinenstr. 45, 70178 Stuttgart, Germany
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Zhou L, Fischer E, Tunca C, Brahms CM, Ersoy C, Granacher U, Arnrich B. How We Found Our IMU: Guidelines to IMU Selection and a Comparison of Seven IMUs for Pervasive Healthcare Applications. SENSORS 2020; 20:s20154090. [PMID: 32707987 PMCID: PMC7435687 DOI: 10.3390/s20154090] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/09/2020] [Revised: 07/12/2020] [Accepted: 07/16/2020] [Indexed: 12/12/2022]
Abstract
Inertial measurement units (IMUs) are commonly used for localization or movement tracking in pervasive healthcare-related studies, and gait analysis is one of the most often studied topics using IMUs. The increasing variety of commercially available IMU devices offers convenience by combining the sensor modalities and simplifies the data collection procedures. However, selecting the most suitable IMU device for a certain use case is increasingly challenging. In this study, guidelines for IMU selection are proposed. In particular, seven IMUs were compared in terms of their specifications, data collection procedures, and raw data quality. Data collected from the IMUs were then analyzed by a gait analysis algorithm. The difference in accuracy of the calculated gait parameters between the IMUs could be used to retrace the issues in raw data, such as acceleration range or sensor calibration. Based on our algorithm, we were able to identify the best-suited IMUs for our needs. This study provides an overview of how to select the IMUs based on the area of study with concrete examples, and gives insights into the features of seven commercial IMUs using real data.
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Affiliation(s)
- Lin Zhou
- Digital Health Center, Hasso Plattner Institute, University of Potsdam, 14482 Potsdam, Germany;
- Correspondence: (L.Z.); (B.A.)
| | - Eric Fischer
- Digital Health Center, Hasso Plattner Institute, University of Potsdam, 14482 Potsdam, Germany;
| | - Can Tunca
- NETLAB, Department of Computer Engineering, Bogazici University, 34342 Istanbul, Turkey; (C.T.); (C.E.)
| | - Clemens Markus Brahms
- Division of Training and Movement Sciences, University of Potsdam, 14469 Potsdam, Germany; (C.M.B.); (U.G.)
| | - Cem Ersoy
- NETLAB, Department of Computer Engineering, Bogazici University, 34342 Istanbul, Turkey; (C.T.); (C.E.)
| | - Urs Granacher
- Division of Training and Movement Sciences, University of Potsdam, 14469 Potsdam, Germany; (C.M.B.); (U.G.)
| | - Bert Arnrich
- Digital Health Center, Hasso Plattner Institute, University of Potsdam, 14482 Potsdam, Germany;
- Correspondence: (L.Z.); (B.A.)
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