Trajectory and Sway Prediction Towards Fall Prevention

TL;DR

This paper introduces a metric for monitoring torso sway to predict and prevent falls, demonstrating strong correlation with perturbations and utilizing visual cues for improved prediction accuracy.

Contribution

It proposes a novel metric for torso sway analysis and a predictive model that incorporates visual scene data for fall prevention applications.

Findings

Torso sway correlates strongly with active perturbations.

The predictive model effectively uses past trajectory and visual cues.

Results show promising potential for fall prevention systems.

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 person's 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.