Journal of NeuroEngineering and Rehabilitation (Dec 2022)
Design and validation of a multi-task, multi-context protocol for real-world gait simulation
- Kirsty Scott,
- Tecla Bonci,
- Francesca Salis,
- Lisa Alcock,
- Ellen Buckley,
- Eran Gazit,
- Clint Hansen,
- Lars Schwickert,
- Kamiar Aminian,
- Stefano Bertuletti,
- Marco Caruso,
- Lorenzo Chiari,
- Basil Sharrack,
- Walter Maetzler,
- Clemens Becker,
- Jeffrey M. Hausdorff,
- Ioannis Vogiatzis,
- Philip Brown,
- Silvia Del Din,
- Björn Eskofier,
- Anisoara Paraschiv-Ionescu,
- Alison Keogh,
- Cameron Kirk,
- Felix Kluge,
- Encarna M. Micó-Amigo,
- Arne Mueller,
- Isabel Neatrour,
- Martijn Niessen,
- Luca Palmerini,
- Henrik Sillen,
- David Singleton,
- Martin Ullrich,
- Beatrix Vereijken,
- Marcel Froehlich,
- Gavin Brittain,
- Brian Caulfield,
- Sarah Koch,
- Anne-Elie Carsin,
- Judith Garcia-Aymerich,
- Arne Kuederle,
- Alison Yarnall,
- Lynn Rochester,
- Andrea Cereatti,
- Claudia Mazzà,
- for the Mobilise-D consortium
Affiliations
- Kirsty Scott
- Department of Mechanical Engineering and Insigneo Institute for in Silico Medicine, The University of Sheffield
- Tecla Bonci
- Department of Mechanical Engineering and Insigneo Institute for in Silico Medicine, The University of Sheffield
- Francesca Salis
- Department of Biomedical Sciences, University of Sassari
- Lisa Alcock
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University
- Ellen Buckley
- Department of Mechanical Engineering and Insigneo Institute for in Silico Medicine, The University of Sheffield
- Eran Gazit
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center
- Clint Hansen
- Department of Neurology, University Medical Center Schleswig-Holstein Campus Kiel
- Lars Schwickert
- Robert Bosch Gesellschaft für Medizinische Forschung
- Kamiar Aminian
- Laboratory of Movement Analysis and Measurement, Ecole Polytechnique Federale de Lausanne
- Stefano Bertuletti
- Department of Biomedical Sciences, University of Sassari
- Marco Caruso
- Department of Biomedical Sciences, University of Sassari
- Lorenzo Chiari
- Department of Electrical, Electronic and Information Engineering «Guglielmo Marconi», University of Bologna
- Basil Sharrack
- Department of Neuroscience and Sheffield NIHR Translational Neuroscience BRC, Sheffield Teaching Hospitals NHS Foundation Trust
- Walter Maetzler
- Department of Neurology, University Medical Center Schleswig-Holstein Campus Kiel
- Clemens Becker
- Robert Bosch Gesellschaft für Medizinische Forschung
- Jeffrey M. Hausdorff
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center
- Ioannis Vogiatzis
- Department of Sport, Exercise and Rehabilitation, Northumbria University Newcastle
- Philip Brown
- Newcastle upon Tyne Hospitals NHS Foundation Trust
- Silvia Del Din
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University
- Björn Eskofier
- Machine Learning and Data Analytics Lab, Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg
- Anisoara Paraschiv-Ionescu
- Laboratory of Movement Analysis and Measurement, Ecole Polytechnique Federale de Lausanne
- Alison Keogh
- Insight Centre for Data Analytics, University College Dublin
- Cameron Kirk
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University
- Felix Kluge
- Machine Learning and Data Analytics Lab, Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg
- Encarna M. Micó-Amigo
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University
- Arne Mueller
- Novartis Institutes of Biomedical Research, Novartis Pharma AG
- Isabel Neatrour
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University
- Martijn Niessen
- McRoberts BV
- Luca Palmerini
- Department of Electrical, Electronic and Information Engineering «Guglielmo Marconi», University of Bologna
- Henrik Sillen
- Digital Health R&D
- David Singleton
- Insight Centre for Data Analytics, University College Dublin
- Martin Ullrich
- Machine Learning and Data Analytics Lab, Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg
- Beatrix Vereijken
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology
- Marcel Froehlich
- Grünenthal GmbH
- Gavin Brittain
- Department of Neuroscience and Sheffield NIHR Translational Neuroscience BRC, Sheffield Teaching Hospitals NHS Foundation Trust
- Brian Caulfield
- Insight Centre for Data Analytics, University College Dublin
- Sarah Koch
- Barcelona Institute for Global Health (ISGlobal)
- Anne-Elie Carsin
- Barcelona Institute for Global Health (ISGlobal)
- Judith Garcia-Aymerich
- Barcelona Institute for Global Health (ISGlobal)
- Arne Kuederle
- Machine Learning and Data Analytics Lab, Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg
- Alison Yarnall
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University
- Lynn Rochester
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University
- Andrea Cereatti
- Department of Biomedical Sciences, University of Sassari
- Claudia Mazzà
- Department of Mechanical Engineering and Insigneo Institute for in Silico Medicine, The University of Sheffield
- for the Mobilise-D consortium
- DOI
- https://doi.org/10.1186/s12984-022-01116-1
- Journal volume & issue
-
Vol. 19,
no. 1
pp. 1 – 12
Abstract
Abstract Background Measuring mobility in daily life entails dealing with confounding factors arising from multiple sources, including pathological characteristics, patient specific walking strategies, environment/context, and purpose of the task. The primary aim of this study is to propose and validate a protocol for simulating real-world gait accounting for all these factors within a single set of observations, while ensuring minimisation of participant burden and safety. Methods The protocol included eight motor tasks at varying speed, incline/steps, surface, path shape, cognitive demand, and included postures that may abruptly alter the participants’ strategy of walking. It was deployed in a convenience sample of 108 participants recruited from six cohorts that included older healthy adults (HA) and participants with potentially altered mobility due to Parkinson’s disease (PD), multiple sclerosis (MS), proximal femoral fracture (PFF), chronic obstructive pulmonary disease (COPD) or congestive heart failure (CHF). A novelty introduced in the protocol was the tiered approach to increase difficulty both within the same task (e.g., by allowing use of aids or armrests) and across tasks. Results The protocol proved to be safe and feasible (all participants could complete it and no adverse events were recorded) and the addition of the more complex tasks allowed a much greater spread in walking speeds to be achieved compared to standard straight walking trials. Furthermore, it allowed a representation of a variety of daily life relevant mobility aspects and can therefore be used for the validation of monitoring devices used in real life. Conclusions The protocol allowed for measuring gait in a variety of pathological conditions suggests that it can also be used to detect changes in gait due to, for example, the onset or progression of a disease, or due to therapy. Trial registration: ISRCTN—12246987.
Keywords
- Digital mobility outcomes
- Technical validation
- Wearable sensors
- Neurological diseases
- Mobility monitoring
