On applying support vector machines to a user authentication method using surface electromyogram signals

• Published in: Artificial life and robotics 2018-03, Vol.23 (1), p.87-93
• Main Authors: Yamaba, Hisaaki, Kurogi, Tokiyoshi, Aburada, Kentaro, Kubota, Shin-Ichiro, Katayama, Tetsuro, Park, Mirang, Okazaki, Naonobu
• Format: Article
• Language: English
• Subjects: Artificial Intelligence; Authentication; Computation by Abstract Devices; Computer Science; Control; Electronic devices; Mechatronics; Muscles; Original Article; Robotics; Security management; Skin; Smartphones; Support vector machines
• ISSN: 1433-5298; 1614-7456
• DOI: 10.1007/s10015-017-0404-z

At present, mobile devices such as tablet-type PCs and smart phones have widely penetrated into our daily lives. Therefore, an authentication method that prevents shoulder surfing is needed. 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 detected over the skin surface, are generated by the electrical activity of muscle fibers during contraction. Muscle movement can be differentiated by analyzing the s-EMG. Taking advantage of the characteristics, we proposed a method that uses a list of gestures as a password in the previous study. In this paper, we introduced support vector machines (SVM) for improvement of the method of identifying gestures. A series of experiments was carried out to evaluate the performance of the SVM based method as a gesture classifier and we also discussed its security.