Assessing real-world gait with digital technology? Validation, insights and recommendations from the Mobilise-D consortium

Journal of NeuroEngineering and Rehabilitation

Research

Anne-Elie Carsin¹  Judith Garcia-Aymerich¹  Sarah Koch¹  Eran Gazit²  Jeffrey M. Hausdorff³  Francesca Salis⁴  Ilaria D’Ascanio⁵  Luca Palmerini⁶  Lorenzo Chiari⁶  Andrea Cereatti⁷  Stefano Bertuletti⁷  Marco Caruso⁷  Ellen Buckley⁸  Kirsty Scott⁸  Claudia Mazzà⁸  Tecla Bonci⁸  Clint Hansen⁹  Walter Maetzler⁹  Beatrix Vereijken^1,10  Basil Sharrack^1,11  Dimitrios Megaritis^1,12  Emily Hume^1,12  Ioannis Vogiatzis^1,12  Henrik Sillén^1,13  Marcel Froehlich^1,14  Alison Keogh^1,15  Brian Caulfield^1,15  David Singleton^1,15  Abolfazl Soltani^1,16  Anisoara Paraschiv-Ionescu^1,16  Kamiar Aminian^1,16  Arne Küderle^1,17  Martin Ullrich^1,17  Bjoern Eskofier^1,17  Felix Kluge^1,18  Martijn Niessen^1,19  Arne Mueller^2,20  Clemens Becker^2,21  Lars Schwickert^2,21  Sara Fernstad^2,22  Hugo Hiden^2,22  Alma Cantu^2,22  Philip Brown^2,23  Cameron Kirk^2,24  M. Encarna Micó-Amigo^2,24  Silvia Del Din^2,25  Lisa Alcock^2,25  Alison J. Yarnall^2,26  Lynn Rochester^2,26 

[1] Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain;Universitat Pompeu Fabra, Barcelona, Catalonia, Spain;CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain;Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel;Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel;Sagol School of Neuroscience and Department of Physical Therapy, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel;Rush Alzheimer’s Disease Center and Department of Orthopaedic Surgery, Rush University Medical Center, Chicago, IL, USA;Department of Biomedical Sciences, University of Sassari, Sassari, Italy;Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy;Department of Electrical, Electronic and Information Engineering «Guglielmo Marconi», University of Bologna, Bologna, Italy;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;Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy;Department of Mechanical Engineering and Insigneo Institute for in Silico Medicine, The University of Sheffield, Sheffield, UK;Department of Neurology, University Medical Center Schleswig-Holstein Campus Kiel, Kiel, Germany;Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, Trondheim, Norway;Department of Neuroscience and Sheffield NIHR Translational Neuroscience BRC, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK;Department of Sport, Exercise and Rehabilitation, Northumbria University Newcastle, Newcastle upon Tyne, UK;Digital Health R&D, AstraZeneca, Stockholm, Sweden;Grünenthal GmbH, Aachen, Germany;Insight Centre for Data Analytics, University College Dublin, Dublin, Ireland;School of Public Health, Physiotherapy and Sports Science, University College Dublin, Dublin, Ireland;Laboratory of Movement Analysis and Measurement, Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland;Machine Learning and Data Analytics Lab, Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany;Machine Learning and Data Analytics Lab, Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany;Novartis Institutes of Biomedical Research, Novartis Pharma AG, Basel, Switzerland;McRoberts BV, The Hague, Netherlands;Novartis Institutes of Biomedical Research, Novartis Pharma AG, Basel, Switzerland;Robert Bosch Gesellschaft für Medizinische Forschung, Stuttgart, Germany;School of Computing, Newcastle University, Newcastle upon Tyne, UK;The Newcastle Upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK;Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK;Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK;National Institute for Health and Care Research (NIHR) Newcastle Biomedical Research Centre (BRC), Newcastle University and The Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK;Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK;National Institute for Health and Care Research (NIHR) Newcastle Biomedical Research Centre (BRC), Newcastle University and The Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK;The Newcastle Upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK;

关键词: Real-world gait;    Algorithms;    DMOs;    Validation;    Wearable sensor;    Walking;    Cadence;    SL;    Digital health;    Accelerometer;   

DOI  :  10.1186/s12984-023-01198-5

 received in 2022-09-21, accepted in 2023-05-26,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundAlthough digital mobility outcomes (DMOs) can be readily calculated from real-world data collected with wearable devices and ad-hoc algorithms, technical validation is still required. The aim of this paper is to comparatively assess and validate DMOs estimated using real-world gait data from six different cohorts, focusing on gait sequence detection, foot initial contact detection (ICD), cadence (CAD) and stride length (SL) estimates.MethodsTwenty healthy older adults, 20 people with Parkinson’s disease, 20 with multiple sclerosis, 19 with proximal femoral fracture, 17 with chronic obstructive pulmonary disease and 12 with congestive heart failure were monitored for 2.5 h in the real-world, using a single wearable device worn on the lower back. A reference system combining inertial modules with distance sensors and pressure insoles was used for comparison of DMOs from the single wearable device. We assessed and validated three algorithms for gait sequence detection, four for ICD, three for CAD and four for SL by concurrently comparing their performances (e.g., accuracy, specificity, sensitivity, absolute and relative errors). Additionally, the effects of walking bout (WB) speed and duration on algorithm performance were investigated.ResultsWe identified two cohort-specific top performing algorithms for gait sequence detection and CAD, and a single best for ICD and SL. Best gait sequence detection algorithms showed good performances (sensitivity > 0.73, positive predictive values > 0.75, specificity > 0.95, accuracy > 0.94). ICD and CAD algorithms presented excellent results, with sensitivity > 0.79, positive predictive values > 0.89 and relative errors < 11% for ICD and < 8.5% for CAD. The best identified SL algorithm showed lower performances than other DMOs (absolute error < 0.21 m). Lower performances across all DMOs were found for the cohort with most severe gait impairments (proximal femoral fracture).Algorithms’ performances were lower for short walking bouts; slower gait speeds (< 0.5 m/s) resulted in reduced performance of the CAD and SL algorithms.ConclusionsOverall, the identified algorithms enabled a robust estimation of key DMOs. Our findings showed that the choice of algorithm for estimation of gait sequence detection and CAD should be cohort-specific (e.g., slow walkers and with gait impairments). Short walking bout length and slow walking speed worsened algorithms’ performances.Trial registration ISRCTN – 12246987.

【 授权许可】

CC BY   
© The Author(s) 2023

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