Adaptive classification in EMG pattern recognition for myoelectric control

Myoelectric control based on pattern recognition technology is largely influenced by intrinsic and extrinsic factors over time in practical application, which often result in performance degradation. The aim of this study is to develop an adaptive classification scheme based on linear discriminant analysis (LDA) for myoelectric control system. The proposed adaptive LDA classifier based on training dataset selection and probability weighting is applied to improve the performance of myoelectric control system in changing environment. Preliminary study on the long-term use in real clinical implementation is conducted to verify the performance of proposed adaptive LDA classifier. The correct recognition rates of motion recognition of the proposed LDA classifier and conventional LDA classifier are compared. The experimental results show that compared with the conventional LDA, the proposed LDA significantly improves the classifier performance. Experiment results show the proposed scheme is a practical approach for developing myoelectric control systems with strong robustness.