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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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Principais autores: Jason Moore, Robert Catena, Lisa Fournier, Pegah Jamali, Peter McMeekin, Samuel Stuart, Richard Walker, Thomas Salisbury, Alan Godfrey
Format: Artigo
Sprog:Inglês
Udgivet: BMC 2024-06-01
Serier:Journal of NeuroEngineering and Rehabilitation
Fag:
Fall risk
Gait analysis
Object detection
Deep learning
Visual attention
Online adgang:https://doi.org/10.1186/s12984-024-01400-2
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