The Influence of Gait Phase on Predicting Lower-Limb Joint Angles

Abstract

Machine learning has seen a rapid increase in applications that harness wearable signals to improve human mobility. Previous work has used machine learning predictors as a means of continuously estimating locomotor intent. Although previous studies perform gait phase classification and joint-level angular prediction, there are currently no studies that compare joint-level prediction performance at various phases of gait. As such, the purpose of this offline study was to analyze how machine learning and deep learning models perform at predicting future joint angles during various phases of gait. EMG, IMU, and joint kinematics collected during level-ground walking from thirty participants and data was separated into six distinct gait phases. Random forest, long short-term memory (LSTM), and bidirectional LSTM was used to predict lower-limb joint angles during the phases of gait. Results indicate that bidirectional LSTM is the most robust performer across the gait cycle, with a mean prediction RMSE of 1.42-5.71 degrees. Our study shows how deep learning methods, such as bidirectional LSTM, can accurately estimate joint angles throughout the gait cycle. Furthermore, we propose future work of deploying models which accurately predict future joint angles throughout the gait cycle for users to sufficiently operate a wearable exoskeleton during locomotion.