Abstract
At the present time, 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 use surface electromyogram (s-EMG) signals, not screen touching. The s-EMG signals, which are generated by the electrical activity of muscle fibers during contraction, are detected over the skin surface. Muscle movement can be differentiated by analyzing the s-EMG. In this paper, a method that uses a list of gestures as a password is proposed. And also, results of experiments are presented that was carried out to investigate the performance of the method extracting feature values from s-EMG signals (using the Fourier transform) adopted in this research. \(Myo^{TM}\), which is the candidate of s-EMG measurement device used in a prototype system for future substantiative experiments, was used in the experiment together with the s-EMG measuring device used in the previous research to investigate its performance.
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References
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
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 Life 1:60–63
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
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
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
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Yamaba, H., Kurogi, A., Kubota, SI. et al. Evaluation of feature values of surface electromyograms for user authentication on mobile devices. Artif Life Robotics 22, 108–112 (2017). https://doi.org/10.1007/s10015-016-0323-4
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DOI: https://doi.org/10.1007/s10015-016-0323-4