Review on EMG Acquisition and Classification Techniques: Towards Zero Retraining in the Influence of User and Arm Position Independence

Abstract

The surface electromyogram (EMG) is widely studied and applied in machine control. Recent methods of classifying hand gestures reported classification rates of over 95%. However, the majority of the studies made were performed on a single user, focusing solely on the gesture classification. These studies are restrictive in practical sense: either focusing on just gestures, multi-user compatibility, or rotation independence. The variations in EMG signals due to these conditions present a challenge to the practical application of EMG devices, often requiring repetitious training per application. To the best of our knowledge, there is little comprehensive review of works done in EMG classification in the combined influence of user-independence, rotation and hand exchange. Therefore, in this paper we present a review of works related to the practical issues of EMG with a focus on the EMG placement, and recent acquisition and computing techniques to reduce training. First, we provided an overview of existing electrode placement schemes. Secondly, we compared the techniques and results of single-subject against multi-subject, multi-position settings. As a conclusion, the study of EMG classification in this direction is relatively new. However the results are encouraging and strongly indicate that EMG classification in a broad range of people and tolerance towards arm orientation is possible, and can pave way for more flexible EMG devices.