基于双线性模型的动作肌电信号用户无关识别研究

doi:10.3969/j.issn.0258-8021.2016.05.003

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Abstract Due to the fact that surface electromyography (sEMG) signals of the same gesture vary from different individuals (user-related) and various gestures produce different sEMG signals (motion-related), the sEMG signals can be treated as the interaction of the two factors. This study utilized bilinear models to extract user-independent features. We first factorized original training features into two factors, and the determination of the factor dimensions can help the reconstructed features have the maximum similarity. When original testing features from a novel user were available, they were used to adapt the two factors with the aid of the aforementioned model parameters and the reconstructed testing features by using the adapted factors were finally sent to the trained classifier for recognition. Eleven subjects were recruited with each performing 4 types of gestures. Three classifiers (linear discriminant classifier, K-nearest neighbor and support vector machine) were employed for the classification of the three tasks, termed as user-dependent cross-time (UDC), original-user-independent (OUI) and bilinear-models-user-independent (BMUI). Experimental results showed that the averaged classification accuracy of BMUI was at least 85% whereas that of OUI was not higher than 75%. The one-way ANOVA analysis demonstrated the significant improvement of BMUI (P<0.001). Besides, although the averaged accuracy of UDC was above 90%, higher than that of BMUI, they were statistically insignificant (P>0.24). The proposed method provided a practical solution to the interaction implementation of myoelectric control system based on gesture recognition techniques, and the training samples could be significantly reduced since each subject will conduct only once experiment for training.

Key words： myoelectric control gesture recognition feature extraction bilinear models

Received: 07 January 2016

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R318

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http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021.2016.05.003 OR http://cjbme.csbme.org/EN/Y2016/V35/I5/526

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