A CNN-SVM combined model for pattern recognition of knee motion using mechanomyography signals

• Published in: Journal of electromyography and kinesiology Vol. 42; pp. 136 - 142
• Main Authors: Wu, Haifeng, Huang, Qing, Wang, Daqing, Gao, Lifu
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 01.10.2018
• Subjects: Convolutional neural network; Knee motion recognition; Mechanomyography; Support vector machine; Mechanomyography; Knee motion recognition; Support vector machine; Convolutional neural network
• ISSN: 1050-6411; 1873-5711
• DOI: 10.1016/j.jelekin.2018.07.005

The commonly used classifiers for pattern recognition of human motion, like backpropagation neural network (BPNN) and support vector machine (SVM), usually implement the classification by extracting some hand-crafted features from the human biological signals. These features generally require the domain knowledge for researchers to be designed and take a long time to be tested and selected for high classification performance. In contrast, convolutional neural network (CNN), which has been widely applied to computer vision, can learn to automatically extract features from the training data by means of convolution and subsampling, but CNN training usually requires large sample data and has the overfitting problem. On the other hand, SVM has good generalization ability and can solve the small sample problem. Therefore, we proposed a CNN-SVM combined model to make use of their advantages. In this paper, we detected 4-channel mechanomyography (MMG) signals from the thigh muscles and fed them in the form of time series signals to the CNN-SVM combined model for the pattern recognition of knee motion. Compared with the common classifier performing the classification with hand-crafted features, the CNN-SVM combined model could automatically extract features using CNN, and better improved the generalization ability of CNN and the classification accuracy by means of combining the SVM. This study would provide reference for human motion recognition using other time series signals and further expand the application fields of CNN.