A Novel Feature Reduction Method for Real-Time EMG Pattern Recognition System

This paper proposes a novel feature reduction approach for real-time electromyogram (EMG) pattern recognition. This study extracts time and frequency information by wavelet packet transform (WPT) coefficients and uses the node energy as the feature to overcome the translation-invariant property of WPT. Then the non-parametric discriminant analysis (NDA) is used for feature reduction. Because of some inherent properties of the packet node energy, the within-class scatter matrix is usually singular in this approach, which makes feature project unavailable. To solve this problem, a recursive algorithm is proposed to discard some feature components that lead to singularity and contain relatively less discriminant information. Finally, the support vector machine (SVM) is used as the classifier and gives the recognition result. The corresponding pattern of the action could be recognized in a millisecond (ms). The experimental results show that the proposed method has strong robustness and good real-time performance.