NDLI: Hand Motion Classification Using a Multi-Channel Surface Electromyography Sensor

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Hand Motion Classification Using a Multi-Channel Surface Electromyography Sensor

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Hand Motion Classification Using a Multi-Channel Surface Electromyography Sensor

Content Provider	MDPI
Author	Tang, Xueyan Liu, Yunhui Lv, Congyi Sun, Dong
Abstract	The human hand has multiple degrees of freedom (DOF) for achieving high-dexterity motions. Identifying and replicating human hand motions are necessary to perform precise and delicate operations in many applications, such as haptic applications. Surface electromyography (sEMG) sensors are a low-cost method for identifying hand motions, in addition to the conventional methods that use data gloves and vision detection. The identification of multiple hand motions is challenging because the error rate typically increases significantly with the addition of more hand motions. Thus, the current study proposes two new methods for feature extraction to solve the problem above. The first method is the extraction of the energy ratio features in the time-domain, which are robust and invariant to motion forces and speeds for the same gesture. The second method is the extraction of the concordance correlation features that describe the relationship between every two channels of the multi-channel sEMG sensor system. The concordance correlation features of a multi-channel sEMG sensor system were shown to provide a vast amount of useful information for identification. Furthermore, a new cascaded-structure classifier is also proposed, in which 11 types of hand gestures can be identified accurately using the newly defined features. Experimental results show that the success rate for the identification of the 11 gestures is significantly high.
File Size	490496
Ending Page	1147
Page Count	18
Starting Page	1130
File Format	PDF
e-ISSN	14248220
DOI	10.3390/s120201130
Journal	Sensors
Issue Number	2
Volume Number	12
Language	English
Publisher Date	2012-01-30
Access Restriction	Open
Subject Keyword	surface electromyography multi-channel hand motion concordance correlation cascaded
Content Type	Text
Resource Type	Article

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