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...

ver descrição completa

Na minha lista:
Detalhes bibliográficos
Publicado no:Scientific Reports
Main Authors: 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
Formato: Artigo
Publicado em: Nature Portfolio 2024-09-01
Assuntos:
Gait
Machine learning
Older adults
Self-supervised learning
Accelerometer
Acesso em linha:https://doi.org/10.1038/s41598-024-71491-3
Tags: Adicionar Tag
Sem tags, seja o primeiro a adicionar uma tag!

Internet

https://doi.org/10.1038/s41598-024-71491-3

Registos relacionados