Hand Movements classification using feature extraction from Surface Electromyography

Content Provider	Semantic Scholar
Author	Najafabadian, Bashir Naghieh, Mahsa Mirzabagherian, Hamed
Copyright Year	2017
Abstract	In this article, the hand movement classification based on surface electromyogram signal processing and feature extraction is presented. A data is collected from NINAPRO database. The database contains the kinematics and surface electromyogram of the muscles of the forearms of 27 healthy subjects during 52 hand movement tasks. After pre-processing of raw data, the EMG signal of each movement, put together based on its labels. The time and frequency features are extracted. Features classified by Support vector machine and k-nearest neighbor classifiers. Result demonstrated that support vector machine is optimal structure for hand movement classification.
File Format	PDF HTM / HTML
Alternate Webpage(s)	http://sweetmaxwell.org/paper/67280_Bashir%20Najafabadian.pdf
Language	English
Access Restriction	Open
Content Type	Text
Resource Type	Article