Recognizing sEMG patterns for interacting with prosthetic manipulation

It is a challenge to achieve a satisfactory rate for the sEMG pattern recognition, which is becoming the main focus of the on-going research in rehabilitation and prosthetics. This chapter introduces nonlinear feature extraction and nonlinear classification approaches to efficiently identify different human hand manipulations based on surface electromyography (sEMG) signals. The recurrence plot is employed to represent dynamical characteristics of sEMG during hand movements as nonlinear features. Fuzzy Gaussian Mixture Models (FGMMs) are proposed and employed as a nonlinear classifier to recognise these hand grasps and in-hand manipulations captured from different subjects. Results from a variety of experiments comparing 14 individual features, 19 multi-features and 4 classifiers demonstrate the proposed nonlinear measures provide essential supplemental information to the good performance in multi-features. It also proves that FGMMs with capacity of modelling nonlinear datasets outperform commonly used approaches including Linear Discriminant Analysis (LDA), Gaussian Mixture Models (GMMs) and Support Vector Machine (SVM). Specially, the best performance with the recognition rate of 96.7% is achieved by FGMMs with the multi-feature combining Willison Amplitude (WAMP) and Determinism (DET).