Classification of surface EMG signals using harmonic wavelet packet transform.

Gang Wang; Zhiguo Yan; Xiao Hu; Hongbo Xie; Zhizhong Wang

doi:10.1088/0967-3334/27/12/001

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Classification of surface EMG signals using harmonic wavelet packet transform.

Gang Wang·Zhiguo Yan·Xiao Hu·Hongbo Xie·Zhizhong Wang

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Abstract

In this paper, an efficient method based on the discrete harmonic wavelet packet transform (DHWPT) is presented to classify surface electromyographic (SEMG) signals. After the relative energy of SEMG signals in each frequency band had been extracted by the DHWPT, a genetic algorithm was utilized to select appropriate features in order to reduce the feature dimensionality. Then, the selected features were used as the input vectors to a neural network classifier to discriminate four types of prosthesis movements. Compared with other classification methods, the proposed method provided high classification accuracy in experimental research. In addition, this method could also save a lot of computational time because the DHWPT has a fast algorithm based on the fast Fourier transform for numerical implementation.

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Animals Cattle Data Interpretation, Statistical Electric Impedance Electrodes In Vitro Techniques Muscle, Skeletal Myography Neuromuscular Diseases Temperature Journal Article Research Support, Non-U.S. Gov't

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Gang Wang, Zhiguo Yan, Xiao Hu, Hongbo Xie, Zhizhong Wang. 2006-10-10. Classification of surface EMG signals using harmonic wavelet packet transform.. https://doi.org/10.1088/0967-3334%2F27%2F12%2F001

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