Sensors 2012, 12(2), 1130-1147; doi:10.3390/s120201130

Hand Motion Classification Using a Multi-Channel Surface Electromyography Sensor

1,* email, 1email, 1email and 2email
Received: 29 November 2011; in revised form: 4 January 2012 / Accepted: 12 January 2012 / Published: 30 January 2012
(This article belongs to the Special Issue Control Systems and Robotics in Bioengineering)
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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.
Keywords: surface electromyography; multi-channel; hand motion; concordance correlation; cascaded
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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MDPI and ACS Style

Tang, X.; Liu, Y.; Lv, C.; Sun, D. Hand Motion Classification Using a Multi-Channel Surface Electromyography Sensor. Sensors 2012, 12, 1130-1147.

AMA Style

Tang X, Liu Y, Lv C, Sun D. Hand Motion Classification Using a Multi-Channel Surface Electromyography Sensor. Sensors. 2012; 12(2):1130-1147.

Chicago/Turabian Style

Tang, Xueyan; Liu, Yunhui; Lv, Congyi; Sun, Dong. 2012. "Hand Motion Classification Using a Multi-Channel Surface Electromyography Sensor." Sensors 12, no. 2: 1130-1147.

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