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

doi:10.1016/j.jelekin.2018.07.005

CORC > 合肥物质科学研究院 > 中国科学院合肥物质科学研究院 > 中科院合肥智能机械研究所

	A CNN-SVM combined model for pattern recognition of knee motion using mechanomyography signals
	Wu, Haifeng 1,2,3; Huang, Qing 2; Wang, Daqing 1,2; Gao, Lifu 2
刊名	JOURNAL OF ELECTROMYOGRAPHY AND KINESIOLOGY
	2018-10-01
卷号	42 页码:136-142
关键词	Convolutional neural network Mechanomyography Knee motion recognition Support vector machine
ISSN号	1050-6411
DOI	10.1016/j.jelekin.2018.07.005
通讯作者	Wu, Haifeng(wuhf@hmfl.ac.cn) ; Gao, Lifu(lifugao@iim.ac.cn)
英文摘要	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.
资助项目	Advance Research Project of Innovation Academy of Robot and Intelligent Manufacturing, Chinese Academy of Sciences[C2016011] ; Key Laboratory of Biomimetic Sensing and Advanced Robot Technology, Anhui Province, China
WOS关键词	EXTERNALLY POWERED PROSTHESIS ; CONVOLUTIONAL NEURAL-NETWORKS ; MUSCLE ; CLASSIFICATION ; INTENTION
WOS研究方向	Neurosciences & Neurology ; Physiology ; Rehabilitation ; Sport Sciences
语种	英语
出版者	ELSEVIER SCI LTD
WOS记录号	WOS:000441876400017
资助机构	Advance Research Project of Innovation Academy of Robot and Intelligent Manufacturing, Chinese Academy of Sciences ; Advance Research Project of Innovation Academy of Robot and Intelligent Manufacturing, Chinese Academy of Sciences ; Advance Research Project of Innovation Academy of Robot and Intelligent Manufacturing, Chinese Academy of Sciences ; Advance Research Project of Innovation Academy of Robot and Intelligent Manufacturing, Chinese Academy of Sciences ; Advance Research Project of Innovation Academy of Robot and Intelligent Manufacturing, Chinese Academy of Sciences ; Advance Research Project of Innovation Academy of Robot and Intelligent Manufacturing, Chinese Academy of Sciences ; Advance Research Project of Innovation Academy of Robot and Intelligent Manufacturing, Chinese Academy of Sciences ; Advance Research Project of Innovation Academy of Robot and Intelligent Manufacturing, Chinese Academy of Sciences ; Advance Research Project of Innovation Academy of Robot and Intelligent Manufacturing, Chinese Academy of Sciences ; 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内容类型	期刊论文
源URL	[http://ir.hfcas.ac.cn:8080/handle/334002/38558]
专题	合肥物质科学研究院_中科院合肥智能机械研究所
通讯作者	Wu, Haifeng; Gao, Lifu
作者单位	1.Univ Sci & Technol China, Hefei 230026, Peoples R China 2.Chinese Acad Sci, Inst Intelligent Machines, Hefei 230031, Peoples R China 3.Chinese Acad Sci, High Field Magnet Lab, Hefei 230031, Peoples R China
推荐引用方式 GB/T 7714	Wu, Haifeng,Huang, Qing,Wang, Daqing,et al. A CNN-SVM combined model for pattern recognition of knee motion using mechanomyography signals[J]. JOURNAL OF ELECTROMYOGRAPHY AND KINESIOLOGY,2018,42:136-142.
APA	Wu, Haifeng,Huang, Qing,Wang, Daqing,&Gao, Lifu.(2018).A CNN-SVM combined model for pattern recognition of knee motion using mechanomyography signals.JOURNAL OF ELECTROMYOGRAPHY AND KINESIOLOGY,42,136-142.
MLA	Wu, Haifeng,et al."A CNN-SVM combined model for pattern recognition of knee motion using mechanomyography signals".JOURNAL OF ELECTROMYOGRAPHY AND KINESIOLOGY 42(2018):136-142.