Falls and Gait Stability

Modeling gait and balance in order to anticipate falls

Description

Falls in the elderly are associated with significant morbidity and mortality. While numerous fall detection devices incorporate AI and machine learning, many of these algorithms have been trained using simulated falls among young healthy volunteers. Video capture studies among the elderly have shown that falls are more often associated with weight transfer (e.g., during movement transitions). We have been focused on developing a contactless, markerless camera system that can track joint positions of individuals as they move. Our goal is to use this camera system to study kinematic movements and ultimately develop a model of human balance and gait stability. Our hope is that we can develop a predictive algorithm to identify when a person’s risk of falling dramatically increases, allowing for an early warning system preventing falls as opposed to only detecting falls.

Relevant Publications

  1. IEEE
    Personalized fall detection system
    Ngu, Anne H, Metsis, Vangelis, Coyne, Shaun, Chung, Brian, Pai, Rachel,  and Chang, Joshua
    In 2020 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) 2020

Relevant Repositories

Under Construction

Funding

NSF Smart and Connected Health