Abstract
In this paper we present an optimal wavelet packet (OWP) method based on Davies-Bouldin criterion for the classification of surface electromyographic signals. To reduce the feature dimensionality of the outputs of the OWP decomposition, the principle components analysis was employed. Then we chose a neural network classifier to discriminate four types of prosthesis movements. The proposed method achieved a mean classification accuracy of 93.75%, which outperformed the method using the energy of wavelet packet coefficients (with mean classification accuracy 86.25%) and the fuzzy wavelet packet method (87.5%).
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Acknowledgments
This work is supported by the National Nature Science Foundation of China under Grant No. 60171006 and the National Basic Research Program (973) of China under Grant No. 2005CB724303.
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Wang, G., Wang, Z., Chen, W. et al. Classification of surface EMG signals using optimal wavelet packet method based on Davies-Bouldin criterion. Med Bio Eng Comput 44, 865–872 (2006). https://doi.org/10.1007/s11517-006-0100-y
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DOI: https://doi.org/10.1007/s11517-006-0100-y