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

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Abstract

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.

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Acknowledgements

This work was partially supported by JSPS KAKENHI Grant number JP17K00186.

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Authors and Affiliations

  1. University of Miyazaki, 1-1, Gakuen Kibanadai-nishi, Miyazaki, Japan

    Hisaaki Yamaba, Tokiyoshi Kurogi, Kentaro Aburada, Tetsuro Katayama & Naonobu Okazaki

  2. Kumamoto Universiry, 1030, Shimo-ogino, Kumamoto, Japan

    Shin-Ichiro Kubota

  3. Kanagawa Institute of Technology, 1030, Shimo-ogino, Atsugi, Kanagawa, Japan

    Mirang Park

Authors
  1. Hisaaki Yamaba
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  2. Tokiyoshi Kurogi
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  3. Kentaro Aburada
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  4. Shin-Ichiro Kubota
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  5. Tetsuro Katayama
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  6. Mirang Park
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  7. Naonobu Okazaki
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Corresponding author

Correspondence to Hisaaki Yamaba.

Additional information

This work was presented in part at the 22nd International Symposium on Artificial Life and Robotics, Beppu, Oita, January 19–21, 2017.

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Yamaba, H., Kurogi, T., Aburada, K. et al. On applying support vector machines to a user authentication method using surface electromyogram signals. Artif Life Robotics 23, 87–93 (2018). https://doi.org/10.1007/s10015-017-0404-z

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  • DOI: https://doi.org/10.1007/s10015-017-0404-z

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