Citation

Wang W, Raitor M, Collins S, Liu CK, Kennedy M. IEEE In. Conf. Robot. Autom. 2023; 2023: 10483-10489.

Copyright

(Copyright © 2023, IEEE Service Center)

DOI

10.1109/icra48891.2023.10161361

PMID

38009123

PMCID

PMC10671274

Abstract

Falls are the leading cause of fatal and non-fatal injuries, particularly for older persons. Imbalance can result from the body's internal causes (illness), or external causes (active or passive perturbation). Active perturbation results from applying an external force to a person, while passive perturbation results from human motion interacting with a static obstacle. This work proposes a metric that allows for the monitoring of the persons torso and its correlation to active and passive perturbations. We show that large changes in the torso sway can be strongly correlated to active perturbations. We also show that we can reasonably predict the future path and expected change in torso sway by conditioning the expected path and torso sway on the past trajectory, torso motion, and the surrounding scene. This could have direct future applications to fall prevention.

RESULTS demonstrate that the torso sway is strongly correlated with perturbations. And our model is able to make use of the visual cues presented in the panorama and condition the prediction accordingly.

Language: en