Empirical copula-Based Templates to Recognize Surface EMG signals of Hand Motions

Current tendency of electromyography (EMG)-based prosthetic hand is to enable the user to perform complex grasps or manipulations with natural muscle movements. In this paper, empirical copula-based templates; including the unified motion template and the state-based motion template, are introduced to identify the naturally contracted surface EMG (sEMG) patterns for hand motion recognition. The unified motion template utilizes a dependence structure as a motion template, which includes one-to-one correlations of the SEMG feature channels with all the sampling points, while the state-based motion template divides the sampling points into different states and takes the union of the dependence structures of the different states. Comparison results have demonstrated that the proposed Empirical Copula-based methods can successfully classify different hand motions from different subjects with better recognition rates than Gaussian mixture models (GMMs). In addition, the state-based motion template has a better performance than the unified motion template especially for the complex hand motions.