Gesture recognition method based on a single-channel sEMG envelope signal

Autor:	Ling Zhang, Shili Liang, Yansheng Wu, Shuangwei Wang, Chai Zongqian, Cao Chunlei
Jazyk:	angličtina
Rok vydání:	2018
Předmět:	Correlation coefficient Computer Networks and Communications Computer science Feature vector 0206 medical engineering lcsh:TK7800-8360 02 engineering and technology k-nearest neighbors algorithm lcsh:Telecommunication Gesture recognition Improved KNN algorithm sEMG lcsh:TK5101-6720 0202 electrical engineering electronic engineering information engineering Envelope signal feature business.industry lcsh:Electronics Soft margin SVM Pattern recognition 020601 biomedical engineering Computer Science Applications Support vector machine Signal Processing 020201 artificial intelligence & image processing Artificial intelligence business Gesture
Zdroj:	EURASIP Journal on Wireless Communications and Networking, Vol 2018, Iss 1, Pp 1-8 (2018)
ISSN:	1687-1499
DOI:	10.1186/s13638-018-1046-0
Popis:	In the past, investigators tend to use multi-channel surface electromyography (sEMG) signal acquisition devices to improve the recognition accuracy for the study of gesture recognition systems based on sEMG. The disadvantages of the method are the increased complexity and the problems such as signal crosstalk. This paper explores a gesture recognition method based on a single-channel sEMG envelope signal feature in the time domain. First, we get the sEMG envelope signal by using a preprocessing circuit. Then, we use the improved method of valid activity segment extraction to find every valid activity segment and extract 15 features from every valid activity segment. Next, we calculate the absolute value of the correlation coefficient between each of the features and target values. After removing the feature with the smaller correlation coefficient, we reserve the 14 features. By the PCA dimensionality reduction algorithm, we transform the 14-dimensional feature into 2-dimensional feature space. Finally, we use the improved KNN algorithm and the soft margin SVM algorithm to complete the classification of five types of gestures. We obtain the gesture recognition rates of 75.8 and 79.4% by using the improved KNN algorithm and the soft margin SVM algorithm.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5a9e7b21f593466d03d9dc086a9ecde3 http://link.springer.com/article/10.1186/s13638-018-1046-0 Zobrazit plný text záznamu Plný text ve formátu PDF Plný text ve formátu HTML
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