Abstract

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Title:: Sign language recognition based on electromyogram signal and muscle deformation signal

Author(s):: ZHANG Xiafeng; KAN Xiu; CAO Le; YANG Dan; ZHANG Wenyan; School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China

Keywords:: Keywords: sign language recognition surface electromyogram signal muscle deformation endpoint detection feature extraction

Abstract:: Abstract: Regarding to sign language and gesture recognitions, a framework for sign language recognition based on electromyogram (EMG) signal and muscle deformation signal is proposed. A signal acquisition system is designed and then is used to collect EMG signal and muscle deformation signal. The noise in original data is removed by filtering and wavelet denoising. The effective active segment of the signal is extracted by the double-threshold endpoint detection method based on energy-to-entropy ratio and the features of EMG signal and muscle deformation signal are extracted. The extracted signal features are fused to form eigenvectors. Finally, the collected sign language motions are identified by support vector machine recognition model based on grid search. The accuracy of the sign language recognition method after signal fusion is improved to 97.2%. Compared with the sign language recognition method which only uses EMG signal, the recognition rate is improved by 9.3% after fusing with muscle deformation signal. The experimental results show that the framework for sign language recognition based on EMG signal and muscle deformation signal has good recognition effect on dynamic sign language and gesture.