Self-supervised learning of wrist-worn daily living accelerometer data improves the automated detection of gait in older adults

Abstract Progressive gait impairment is common among aging adults. Remote phenotyping of gait during daily living has the potential to quantify gait alterations and evaluate the effects of interventions that may prevent disability in the aging population. Here, we developed ElderNet, a self-supervis...

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Главные авторы:	Yonatan E. Brand, Felix Kluge, Luca Palmerini, Anisoara Paraschiv-Ionescu, Clemens Becker, Andrea Cereatti, Walter Maetzler, Basil Sharrack, Beatrix Vereijken, Alison J. Yarnall, Lynn Rochester, Silvia Del Din, Arne Muller, Aron S. Buchman, Jeffrey M. Hausdorff, Or Perlman
Формат:	Artigo
Язык:	Inglês
Опубликовано:	Nature Portfolio 2024-09-01
Серии:	Scientific Reports
Предметы:	Gait Machine learning Older adults Self-supervised learning Accelerometer
Online-ссылка:	https://doi.org/10.1038/s41598-024-71491-3
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