Abstract
Surface electromyography (sEMG) signals can reflect the body motion information and are widely used in military, medical rehabilitation, industrial production. The lower limb motion classification mainly includes feature extraction and classification model establishment. Firstly, we proposed a feature extraction method based on the wavelet packet transform (WPT) and principal component analysis (PCA). We used the wavelet packet method to decompose the sEMG signals of three muscles in the lower limb and got the 24-dimensional eigenvector. To reduce the calculation and improve the speed of the classification model, we used the PCA method to reduce the dimension of the feature vector and got the 3-dimensional eigenvector. Then, we proposed a method based on the scale unscented Kalman filter (SUKF) and neural network (NN) for lower limb motion classification. Through the scale correction unscented transform (SCUT) could optimize the neural network weight and improve lower limb motion classification accuracy. Finally, the experimental results showed that the average accuracy was 93.7%. Compared with the backpropagation neural network (BPNN) and wavelet neural network (WNN), this method could improve the accuracy and reliability of the lower limb motion classification.
Highlights
The Surface electromyography (sEMG) is the bioelectric signal produced by muscle activity
We proposed a method for lower limb motion classification based on improved wavelet packet transform and scale unscented Kalman neural network
The wavelet packet transform was used to extract the energy features of the sEMG signal from the three channels, and the feature was reduced by the principal component analysis (PCA) method
Summary
The sEMG is the bioelectric signal produced by muscle activity. It contains much information about muscle activity and has the advantages of noninvasive recording [1]. The above research could improve the classification accuracy of the lower limb motion, they generate high-dimensional feature vectors, which introduce noise interference and reduce the stability of the model. We proposed a scale unscented Kalman neural network (SUKFNN) lower limb motion classification method, which improved the stability and classification accuracy. As far as the author knew, the wavelet packet transforms combined with the PCA feature extraction method and scale unscented Kalman neural network classification method proposed in this paper was the first time applied to the lower limb motion classification. We proposed a SUKFNN method to establish a lower limb motion classification model to classify five movements. We proposed an sEMG signal classification method based on a scale unscented Kalman neural network and given the design process of the SUKFNN algorithm
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