Muscle synergies for reliable classification of arm motions using myoelectric interface

Synergistic activation of muscles are considered to be the phenomenon by which the central nervous system simplifies its control strategy. Muscle synergies are neurally encoded and considered robust to be able to adapt for various external dynamics. This paper presents a myoelectric-based interface to identify and classify motions of the upper arm involving the shoulder and elbow. We contrast performance of the decoder while using time domain and synergy features. The decoder is trained using extreme learning machine algorithm, and online testing is performed in a virtual environment. Better classification accuracy for online control is obtained while using muscle synergy features. The results indicate better online performance compared to offline performance while using synergy features to classify movements, indicating generalization to dynamic situations and robustness of control.