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실시간 근전도 패턴인식을 위한 특징투영 기법에 관한 연구

A Study on Feature Projection Methods for a Real-Time EMG Pattern Recognition


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 EMC 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 EMC 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 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 pattern recognition 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 generation of control commands for myoelectric hand, are completed within 97 msec. These results confirm that our method is applicable to real-time EMG pattern recognition far myoelectric hand control.

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이 논문을 인용한 문헌 (1)

  1. Park, Se-Hoon ; Hong, Beom-Ki ; Kim, Jong-Kwon ; Hong, Eyong-Pyo ; Mun, Mu-Seong 2011. "Development of the Myoelectric Hand with a 2 DOF Auto Wrist Module" 제어·로봇·시스템학회 논문지 = Journal of institute of control, robotics and systems, 17(8): 824~832 


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