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

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-...

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Bibliographic Details
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
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Summary: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.
ISSN:1433-5298
1614-7456
DOI:10.1007/s10015-017-0404-z