Learning a Control Policy for Fall Prevention on an Assistive Walking Device

TL;DR

This paper introduces a method to enhance assistive walking devices with fall prevention capabilities by developing a fall predictor and recovery policy using onboard sensors, improving safety for seniors.

Contribution

It presents a novel approach to augment existing walking devices with fall prediction and recovery policies based on a robust human gait model and simulation evaluation.

Findings

Human walking policy generates realistic gait sequences.

Augmented device can recover from external perturbations in simulation.

Provides a quantitative evaluation method for assistive device design.

Abstract

Fall prevention is one of the most important components in senior care. We present a technique to augment an assistive walking device with the ability to prevent falls. Given an existing walking device, our method develops a fall predictor and a recovery policy by utilizing the onboard sensors and actuators. The key component of our method is a robust human walking policy that models realistic human gait under a moderate level of perturbations. We use this human walking policy to provide training data for the fall predictor, as well as to teach the recovery policy how to best modify the person's gait when a fall is imminent. Our evaluation shows that the human walking policy generates walking sequences similar to those reported in biomechanics literature. Our experiments in simulation show that the augmented assistive device can indeed help recover balance from a variety of external perturbations. We also provide a quantitative method to evaluate the design choices for an assistive device.