Gaussian process gait trajectory learning and generation of collision-free motion for assist-as-needed rehabilitation

Abstract

This paper introduces an approach to generate ground-collision-free gait motion by learning a statistical model of walking motion and applies assist-as-needed (AAN) training scheme in learned statistical model which is efficient for robotic gait rehabilitation. The method utilizes a nonlinear dimensionality reduction technique, which is based on Gaussian process, to construct the model using gait motion data obtained from several dozens of healthy subjects. The model is a common, averaged in statistical sense, low-dimensional representation of walking motion. Using the model, it is possible to generate a ground-collision-free gait trajectory at an arbitrary walking speed for a subject on the gait rehabilitation robot, and apply AAN training paradigm around the generated motion. We simulate the framework of learning and generation of motion with gait data from 50 healthy subjects, who walked on a motorized treadmill at 3 different speeds.