Comprehensive Swing Leg Motion Predictor for Steady and Transient Walking Conditions

Abstract

Data-driven methods based on neural networks are becoming more widespread for predicting human lower-limb motion. Until now, however, actual examples have focused on only a handful, steady locomotion behaviors. Here we explore if neural network predictors can simultaneously cover many more behaviors including transient ones. Training four common types of predictor networks on a large data set of human gait, we find that they all accommodate these behaviors similarly well, maintaining prediction errors of a few centimeters (lower-limb joint positions) and degrees (joint angles) when tested on data of previously seen subjects. We further observe that although the prediction quality drops for data of unseen subjects, overall, the predicted and actual lower-limb motions remain well aligned. While the predictors demonstrated here cover the largest range of locomotion behaviors reported to date, we achieve this improvement not by better network design but simply by training on more data. This outcome clearly supports the notion that the fastest route to obtain truly general network predictors of lower-limb motion is by focusing time and effort on the rapid growth and sharing of data sets of locomotion behaviors encountered in daily life.