  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.
