A Supervised Feature Projection for Real-Time Multifunction Myoelectric Hand Control

Abstract

EMG pattern recognition is essential for the control of a multifunction myoelectric hand. The main goal of this study is to develop an efficient feature projection method for EMG pattern recognition. To this end, we propose a linear supervised feature projection that utilizes linear discriminant analysis (LDA). We first perform wavelet packet transform (WPT) to extract the feature vector from four channel EMG signals. For dimensionality reduction and clustering of the WPT features, the LDA incorporates class information into the learning procedure and finds a linear matrix to maximize the class separability for the projected features. Finally, the multilayer perceptron (MLP) classifies the LDA-reduced features into nine hand motions. To evaluate the performance of LDA for the WPT features, we compare LDA with three other feature projection methods. From a visualization and quantitative comparison, we show that LDA has better performance for the class separability, and the LDA-projected features improve the classification accuracy with a short processing time. We implemented a real-time control system for a multifunction myoelectric hand. In experiment, we show that the proposed method achieves 97.2% recognition accuracy, and that all processes, including the myoelectric hand control, are completed within 97 msec.