Comparison of sEMG-based feature extraction and hand motion classification methods

Abstract

The myoelectric prosthetic hand is regard as a useful tool to provide convenience for the upper amputees. There are two key challenges for the control of myoelectric prosthetic hand, one is the surface electromyogram (sEMG) feature extraction, the other is the identification of hand motions. In this paper, we analyzed the influence of feature selection from four feature sets and determined the most appropriate feature in time-frequency domain. Furthermore, we utilized two methods of wavelet neural network (WNN) and support vector machines (SVMs) to identify six kinds of hand motions. We trained the WNN using a hybrid method which consists of back-propagation (BP) and least mean square (LMS), and trained SVMs with grid search (GS) and cross validation (CV) for getting the prediction model. The classification results show that the training time of WNN for hand motion classification is longer than that of SVMs. However, comparing with SVMs, the classifier of WNN has the following significant performance: 1) less identification time; 2) more robustness; 3) higher accuracy rate.