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.
Highlights
Aside from the conventional sensors and vision methods, the use of biological surface electromyography sensors is a low-cost method for detecting and identifying human motions, such as hand and limb motions
The identification of human hand motions is relatively difficult because the hand has more degrees of freedom (DOF) than the other limbs, and the muscles responsible for finger activation are densely distributed
The current study aims to develop an accurate surface electromyography (sEMG)-based sensing system by describing methods for identifying multiple gestures to reduce the recognition error, which is typically high as the number of predefined gestures increases
Summary
Aside from the conventional sensors and vision methods, the use of biological surface electromyography (sEMG) sensors is a low-cost method for detecting and identifying human motions, such as hand and limb motions. The placement of sEMG electrodes is a critical issue for the successful identification of hand motions. The redundant channels of multi-channel sEMG sensor rings generate vast amounts of information, and the manner by which this information is harnessed is a new research issue. The second contribution of the current study is the in-depth investigation on the relationship of the different channels to define a new concordance correlation feature. The classifier is another critical issue for the successful identification of hand motions. The third contribution of the current study is the improvement on the statistical classification method by proposing a new cascaded-structure classifier.
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