A Gait Patterns Recognition Approach Based on Surface Electromyography and Three-Axis Acceleration Signals

Abstract

In this paper, an approach based on combining surface electromyography(sEMG) and three-Axis acceleration(ACC) signal was proposed to recognize 5 different kinds basis daily gait patterns, including walking on the ground, going up stairs, going down stairs, going up slope and going down slope. Firstly, the gait related sEMG signal and three-Axis ACC signal were collected from the lower limbs of subjects. Secondly, the de-noising of sEMG signal was finished and the segmentation of the fusion signal was done. Thirdly, the features of fusion signal were extracted. Finally, a classifier based on 2-stream hidden Markov model (HMM) was built to recognize 5 kinds of basis daily gait patterns. The experiment obtained an average recognition accuracy of 94.32%, which is 4.15% higher than the accuracy by adopting sEMG signal only (Average 90.17%), and 9.60% higher than the accuracy by adopting ACC signal only (Average 84.72%). The result demonstrated that it can improve the recognition accuracy of gait patterns effectively to combine sEMG signal and three-Axis ACC signal.