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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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References

  1. Tamura H, Okumura D, Tanno K (2007) A study on motion recognition without FFT from surface-EMG (In Japanese). IEICE Part D J90–D(9):2652–2655

    Google Scholar 

  2. Yamaba H, Nagatomo S, Aburada K et al (2015) An authentication method for mobile devices that is independent of tap-operation on a touchscreen. J Robot Netw Artif Lifed 1:60–63

    Article  Google Scholar 

  3. Yamaba, H, Kurogi, T, Kubota, S, et al (2016) An attempt to use a gesture control armband for a user authentication system using surface electromyograms. In: Proceedings of 19th international symposium on artificial life and robotics, pp 342–245

  4. Yamaba H, Kurogi T, Kubota S et al (2017) Evaluation of feature values of surface electromyograms for user authentication on mobile devices. Artif Life Robot 22:108–112

    Article  Google Scholar 

  5. Kita Y, Okazaki N, Nishimura H et al (2014) Implementation and evaluation of shoulder-surfing attack resistant users (in Japanese). IEICE Part D J97–D(12):1770–1784

    Google Scholar 

  6. Kita Y, Kamizato K, Park M et al (2014) A study of rhythm authentication and its accuracy using the self-organizing maps (in Japanese). Proc DICOMO 2014:1011–1018

    Google Scholar 

  7. Tamura H, Goto T, Okumura D et al (2009) A study on the s-EMG pattern recognition using neural network. IJICIC 5(12):4877–4884

    Google Scholar 

  8. http://www.myo.com. Accessed 30 Aug 2017

  9. https://www.kickstarter.com/projects/belfio/deus-ex-aria-the-evolution-of-smartwatch-control. Accessed 30 Aug 2017

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Acknowledgements

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

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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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