Mobilise-D insights to estimate real-world walking speed in multiple conditions with a wearable device
Cameron Kirk, Arne Küderle, M. Encarna Micó-Amigo, Tecla Bonci, Anisoara Paraschiv-Ionescu, Martin Ullrich, Abolfazl Soltani, Eran Gazit, Francesca Salis, Lisa Alcock, Kamiar Aminian, Clemens Becker, Stefano Bertuletti, Philip M. Brown, Ellen Buckley, Alma Cantu, Anne‐Elie Carsin, Marco Caruso, Brian Caulfield, Andrea Cereatti, Lorenzo Chiari, Ilaria D’Ascanio, Judith García‐Aymerich, Clint Hansen, Jeffrey M. Hausdorff, Hugo Hiden, Emily Hume, Alison Keogh, Felix Kluge, Sarah Koch, Walter Maetzler, Dimitrios Megaritis, Arne Mueller, Martijn Niessen, Luca Palmerini, Lars Schwickert, Kirsty Scott, Basil Sharrack, Henrik Sillén, David Singleton, Beatrix Vereijken, Ioannis Vogiatzis, Alison J. Yarnall, Lynn Rochester, Claudia Mazzà, Bjoern M. Eskofier, Silvia Del Din, Francesca Bottin, Lorenzo Chiari, Cristina Curreli, Ilaria D’Ascanio, Giorgio Davico, Roberta De Michele, Giuliano Galimberti, Luca Palmerini, Saverio Ranciati, Luca Reggi, Marco Viceconti, Amgen, Lucia D’Apote, Jules Desmond, Megan Doyle, Mary Elliot-Davey, Gilles Gnacadja, Anja Kassner, Beat Knüsel, Monika Pocrzepa, Nicolas Pourbaix, Hoi-Shen Radcliffe, Lening Shen, Jennifer Simon, AstraZeneca AB, Jesper Havsol, Diana Jarretta, Magnus Jörntén‐Karlsson, Pierre Mugnier, Solange Corriol Rohou, Gabriela Luporini Saraiva, Henrik Sillén, Bayer Aktiengesellschaft, Michael Karl Boettger, Igor Knezevic, Frank Kramer, Paolo Piraino, H Trübel, Hajar Ahachad, Hubert Blain, Sylvie Broussous, François Canovas, Florent Cerret, Louis Dagneaux, Valérie Driss, Florence Galtier, Charlote Kaan, Stéphanie Miot, Eva Murauer, Anne-Sophie Vérissimo, Christian-Albrechts-Universität, Daniela Berg, Kirsten Emmert
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.