Toward Personal Authentication Using Gesture Classification with EMG Data

Abstract

We examine a possibility of personal authentication using forearm surface EMG (s-EMG) during gesture operation. The s-EMG was measured 10 times from the 5 subjects during performing 6 gestures. In our previous research, we performed the gesture identification by using Support Vector Machine (SVM). As the feature values, we used the maximum and minimum values and their times of the time domain data during gesture. As the result, the identification rate was 66.7%. In this paper, to improve the identification rate, we increased the future values which we used, and we introduced the selection algorithms of the important feature values based on the random forest. As the result, the identification rate by using SVM was improved to over 80%. Moreover, the previous research claimed that the feature values in frequency domain was not effective. We found that some feature values in the frequency domain was effective for the gesture identification.