A multi-sensor wearable system for the assessment of diseased gait in real-world conditions

Frontiers in Bioengineering and Biotechnology

Bioengineering and Biotechnology

Eran Gazit¹  Jeffrey M. Hausdorff¹  Lars Schwickert²  Clemens Becker²  Stefano Bertuletti³  Ugo Della Croce³  Francesca Salis³  Ilaria D’Ascanio⁴  Luca Palmerini⁵  Lorenzo Chiari⁵  Ellen Buckley⁶  Claudia Mazzà⁶  Tecla Bonci⁶  Kirsty Scott⁶  Clint Hansen⁷  Walter Maetzler⁷  Beatrix Vereijken⁸  Basil Sharrack⁹  Emily C. Hume^1,10  Ioannis Vogiatzis^1,10  Brian Caulfield^1,11  David Singleton^1,11  Anne-Elie Carsin^1,12  Judith Garcia-Aymerich^1,12  Sarah Koch^1,12  Andrea Cereatti^1,13  Marco Caruso^1,13  Anisoara Paraschiv-Ionescu^1,14  Kamiar Aminian^1,14  Arne Kuederle^1,15  Bjoern M. Eskofier^1,15  Felix Kluge^1,16  Philip Brown^1,17  Arne Mueller^1,18  Isabel Neatrour^1,19  Cameron Kirk^1,19  Encarna M. Micó-Amigo^1,19  Silvia Del Din^2,20  Lisa Alcock^2,20  Lynn Rochester^2,21  Alison J. Yarnall^2,21 

[1] Centre for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Centre, Tel Aviv, Israel;Department for Geriatric Rehabilitation, Robert-Bosch-Hospital, Stuttgart, Germany;Department of Biomedical Sciences, University of Sassari, Sassari, Italy;Interuniversity Centre of Bioengineering of the Human Neuromusculoskeletal System (IuC BoHNes), Sassari, 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 Centre for Industrial Research (CIRI-SDV), University of Bologna, Bologna, Italy;Department of Mechanical Engineering, Insigneo Institute for In Silico Medicine, The University of Sheffield, Sheffield, United Kingdom;Department of Neurology, University Medical Centre Schleswig-Holstein Campus Kiel and Kiel University, 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, United Kingdom;Department of Sport, Exercise and Rehabilitation, Faculty of Health and Life Sciences, Northumbria University, Northumbia, United Kingdom;Insight Centre for Data Analytics, University College Dublin, Dublin, Ireland;Instituto de Salud Global Barcelona, Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain;Faculty of Health and Life Sciences, Universitat Pompeu Fabra, Barcelona, Spain;CIBER Epidemiología y Salud Pública, Madrid, Spain;Interuniversity Centre of Bioengineering of the Human Neuromusculoskeletal System (IuC BoHNes), Sassari, Italy;Department of Electronics and Telecommunications, Politecnico Di Torino, Torino, Italy;Laboratory of Movement Analysis and Measurement, Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland;Machine Learning and Data Analytics Lab, Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany;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;Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle Upon Tyne, United Kingdom;Novartis Institutes of Biomedical Research, Novartis Pharma AG, Basel, Switzerland;Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne, United Kingdom;Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne, United Kingdom;National Institute for Health and Care Research (NIHR) Newcastle Biomedical Research Centre (BRC), Newcastle University, Newcastle Upon Tyne, United Kingdom;Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne, United Kingdom;National Institute for Health and Care Research (NIHR) Newcastle Biomedical Research Centre (BRC), Newcastle University, Newcastle Upon Tyne, United Kingdom;Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle Upon Tyne, United Kingdom;

关键词: gait analysis;    IMU;    wearable sensors;    ecological conditions;    pressure insoles;    distance sensors;    spatial-temporal gait parameters;   

DOI  :  10.3389/fbioe.2023.1143248

 received in 2023-01-13, accepted in 2023-03-30,  发布年份 2023

来源: Frontiers

【 摘 要 】

Introduction: Accurately assessing people’s gait, especially in real-world conditions and in case of impaired mobility, is still a challenge due to intrinsic and extrinsic factors resulting in gait complexity. To improve the estimation of gait-related digital mobility outcomes (DMOs) in real-world scenarios, this study presents a wearable multi-sensor system (INDIP), integrating complementary sensing approaches (two plantar pressure insoles, three inertial units and two distance sensors).Methods: The INDIP technical validity was assessed against stereophotogrammetry during a laboratory experimental protocol comprising structured tests (including continuous curvilinear and rectilinear walking and steps) and a simulation of daily-life activities (including intermittent gait and short walking bouts). To evaluate its performance on various gait patterns, data were collected on 128 participants from seven cohorts: healthy young and older adults, patients with Parkinson’s disease, multiple sclerosis, chronic obstructive pulmonary disease, congestive heart failure, and proximal femur fracture. Moreover, INDIP usability was evaluated by recording 2.5-h of real-world unsupervised activity.Results and discussion: Excellent absolute agreement (ICC >0.95) and very limited mean absolute errors were observed for all cohorts and digital mobility outcomes (cadence ≤0.61 steps/min, stride length ≤0.02 m, walking speed ≤0.02 m/s) in the structured tests. Larger, but limited, errors were observed during the daily-life simulation (cadence 2.72–4.87 steps/min, stride length 0.04–0.06 m, walking speed 0.03–0.05 m/s). Neither major technical nor usability issues were declared during the 2.5-h acquisitions. Therefore, the INDIP system can be considered a valid and feasible solution to collect reference data for analyzing gait in real-world conditions.

【 授权许可】

Unknown   
Copyright © 2023 Salis, Bertuletti, Bonci, Caruso, Scott, Alcock, Buckley, Gazit, Hansen, Schwickert, Aminian, Becker, Brown, Carsin, Caulfield, Chiari, D’Ascanio, Del Din, Eskofier, Garcia-Aymerich, Hausdorff, Hume, Kirk, Kluge, Koch, Kuederle, Maetzler, Micó-Amigo, Mueller, Neatrour, Paraschiv-Ionescu, Palmerini, Yarnall, Rochester, Sharrack, Singleton, Vereijken, Vogiatzis, Della Croce, Mazzà and Cereatti and for the Mobilise-D consortium.