The classification of movement intention through machine learning models: the identification of significant time-domain EMG features.

ABSTRACT: Electromyography (EMG) signal is one of the extensively utilised biological signals for predicting human motor intention, which is an essential element in human-robot collaboration platforms. Studies on motion intention prediction from EMG signals have often been concentrated on either classification and regression models of muscle activity. In this study, we leverage the information from the EMG signals, to detect the subject's intentions in generating motion commands for a robot-assisted upper limb rehabilitation platform. The EMG signals are recorded from ten healthy subjects' biceps muscle, and the movements of the upper limb evaluated are voluntary elbow flexion and extension along the sagittal plane. The signals are filtered through a fifth-order Butterworth filter. A number of featu

SUBMITTER: Mohd Khairuddin I 

PROVIDER: S-EPMC7959624 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature