Toward Personal Authentication using Gesture Classification by using SVM and 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 five subjects during performing the 6 gestures. In our previous research, we performed the gesture identiflcation by using SVM. As the feature values, we use the maximum and minimum values and their times of the time domain data during gesture. As the result, the identiflcation rate was 66.7%. In this paper, to improve the identiflcation rate, we increase the future values which we use, and we introduce the selection algorithms of the important feature values, ANOVA and random forest. As the result, the identiflcation rate is improved to over 70% with both selection algorithms. Moreover, the previous research claimed that the feature values in frequency domain was not effective. However, we found that some feature values in frequency domain was effective.