Enhancing fall risk assessment: instrumenting vision with deep learning during walks

Abstract Background Falls are common in a range of clinical cohorts, where routine risk assessment often comprises subjective visual observation only. Typically, observational assessment involves evaluation of an individual’s gait during scripted walking protocols within a lab to identify deficits t...

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Detalles Bibliográficos

Principais autores:	Jason Moore, Robert Catena, Lisa Fournier, Pegah Jamali, Peter McMeekin, Samuel Stuart, Richard Walker, Thomas Salisbury, Alan Godfrey
Formato:	Artigo
Idioma:	Inglês
Publicado:	BMC 2024-06-01
Series:	Journal of NeuroEngineering and Rehabilitation
Assuntos:	Fall risk Gait analysis Object detection Deep learning Visual attention
Acceso en liña:	https://doi.org/10.1186/s12984-024-01400-2
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