Wrist Motion Classification Using Flexible sEMG Sensors in Different Feature Conditions Based on Machine Learning

Electromyography (EMG) is a bioelectrical signal to reflect human intention before actual motion occurs. EMG has been widely used in human-machine interaction such as robot control, rehabilitation and health monitoring. In this work, we have designed an intelligent approach for wrist motion classification based on EMG signals. Since commercial electrodes cannot maintain good contact with skin during deformation, we have utilized a new type of fabricated flexible electrodes. With these electrodes, high-quality signals can be acquired. And the machine learning methods have been utilized to classify the extracted feature sets. Four different feature conditions have been compared. In the condition of six EMG features, we have identified four wrist gestures including wrist flexion, extension, radial deviation, and ulnar deviation with the best accuracy of 92.26%.