Exploration and Comparison of Locomotion Mode Recognition Models for Prosthetic Gait

Abstract

Establishing generalizable models for locomotion mode recognition (LMR) of prosthetic gait can be challenging due to limited access of sufficient labelled datasets. Hence, subject-specific models continue to be primarily used. However, there are no studies that investigated the effect of reducing the amount of training data that is presented to the machine learning model during training. Additionally, previously validated LMR models for prosthetic gait primarily used LDA classifiers. However, literature suggests that RF models may improve overall accuracy based on able-body validation. Therefore, to address those gaps, this study compared the performance of LDA and RF models for prosthetic gait and classifiers to LDA. Varied test size ratios data were evaluated to assess the trade-off between performance and amounts of training data.