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Real-World Gait Detection Using a Wrist-Worn Inertial Sensor: Validation Study

Real-World Gait Detection Using a Wrist-Worn Inertial Sensor: Validation Study

The optimized versions of the Brand (2022) and Paraschiv-Ionescu (2019) algorithms had a performance index above 0.7 for all groups, with the Brand (2022) algorithm showing the highest performance (Figure 1). In the following, the wrist results for the optimized algorithm versions are reported. Performance of assessed algorithms based on a disease group level (n=6). Individual data points are highlighted for each disease group as an overlay.

Felix Kluge, Yonatan E Brand, M Encarna Micó-Amigo, Stefano Bertuletti, Ilaria D'Ascanio, Eran Gazit, Tecla Bonci, Cameron Kirk, Arne Küderle, Luca Palmerini, Anisoara Paraschiv-Ionescu, Francesca Salis, Abolfazl Soltani, Martin Ullrich, Lisa Alcock, Kamiar Aminian, Clemens Becker, Philip Brown, Joren Buekers, Anne-Elie Carsin, Marco Caruso, Brian Caulfield, Andrea Cereatti, Lorenzo Chiari, Carlos Echevarria, Bjoern Eskofier, Jordi Evers, Judith Garcia-Aymerich, Tilo Hache, Clint Hansen, Jeffrey M Hausdorff, Hugo Hiden, Emily Hume, Alison Keogh, Sarah Koch, Walter Maetzler, Dimitrios Megaritis, Martijn Niessen, Or Perlman, Lars Schwickert, Kirsty Scott, Basil Sharrack, David Singleton, Beatrix Vereijken, Ioannis Vogiatzis, Alison Yarnall, Lynn Rochester, Claudia Mazzà, Silvia Del Din, Arne Mueller

JMIR Form Res 2024;8:e50035

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