Evaluation of manual alphabets based gestures for a user authentication method using s-emg

Abstract

At the present time, since mobile devices such as tablet-type PCs and smart phones have widely penetrated into our daily lives, an authentication method that prevents shoulder surfing attacks comes to be important. We are investigating a new user authentication method for mobile devices that uses surface electromyogram (s-EMG) signals, not screen touching. The s-EMG signals, which are generated by the electrical activity of muscle fibers during contraction, can be detected over the skin surface, and muscle movement can be differentiated by analyzing the s-EMG signals. Taking advantage of the characteristics, we proposed a method that uses a list of gestures as a password in the previous study. In order to realize this method, we have to prepare a sufficient number of gestures that are used to compose passwords. In this paper, we adopted fingerspelling as candidates of such gestures. We measured s-EMG signals of manual kana of The Japanese Sign Language syllabary and evaluated their potential as the important element of the user authentication method.