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Online Finger Gesture Recognition Using Surface Electromyography Signals

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DOI: 10.4236/jsip.2013.42013    8,410 Downloads   10,480 Views   Citations
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ABSTRACT

The analysis on the online finger gesture recognition using multi-channel sEMG signals was explored in this paper. Nine types of gestures were applied to be identified, involving six kinds of numerical finger gestures and three kinds of hand gestures. The time domain parameters were extracted to be the features. And then, the probabilistic neural network was utilized to classify the proposed gestures with the extracted features. The experimental results showed that most of gestures could acquire the acceptable classification performance and a few elaborate gestures were hard to acquire the effective identification.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

Q. Li and B. Li, "Online Finger Gesture Recognition Using Surface Electromyography Signals," Journal of Signal and Information Processing, Vol. 4 No. 2, 2013, pp. 101-105. doi: 10.4236/jsip.2013.42013.

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