Locomotion Mode Recognition for Walking on Three Terrains Based on sEMG of Lower Limb and Back Muscles

Abstract

Gait phase detection on different terrains is an essential procedure for amputees with a lower limb assistive device to restore walking ability. In the present study, the intent recognition of gait events on three terrains based on sEMG was presented. The class separability and robustness of time, frequency, and time-frequency domain features of sEMG signals from five leg and back muscles were quantitatively evaluated by statistical analysis to select the best features set. Then, ensemble learning method that combines the outputs of multiple classifiers into a single fusion-produced output was implemented. The results obtained from data collected from four human participants revealed that the light gradient boosting machine (LightGBM) algorithm has an average accuracy of 93.1%, a macro-F1 score of 0.929, and a calculation time of prediction of 15 ms in discriminating 12 different gait phases on three terrains. This was better than traditional voting-based multiple classifier fusion methods. LightGBM is a perfect choice for gait phase detection on different terrains in daily life.

Keywords

• locomotion mode recognition
• sEMG
• ensemble learning
• LightGBM