@article{oai:miyazaki-u.repo.nii.ac.jp:00005685,
 author = {黒木, 聡舜 and Kurogi, Tokiyoshi and Yamaba, Hisaaki and 山場, 久昭 and Aburada, Kentaro and 油田, 健太郎 and Okazaki, Naonobu and 岡崎, 直宣 and 黒木, 聡舜 and Kurogi, Tokiyoshi},
 journal = {宮崎大学工学部紀要, Memoirs of Faculty of Engineering, University of Miyazaki},
 month = {Jul},
 note = {At the present time, mobile devices such as tablet-type PCs and smart phones have widely penetrated into our daily lives. This movement caused a new threat that a stranger takes a peek at our authentication operations on our touch screens and steals our passwords and steals our important information and data in our mobile devices. This forced us to develop new authentication method that can prevent this sort of crime called a shoulder surfing attack. We have investigated a new user authentication method for mobile devices that uses surface electromyogram (s-EMG) signals, not screen touching. The s-EMG signals, which are generated by the electrical activity of muscle fibers during contraction, can be used to identify who generated the signals and which gesture he made. We introduced a pass-gesture, which is a list of hand signals, to realize the s-EMG based authentication method. In this paper, we propose two methods to compare two s-EMG signals and judge whether the two were made by the same gesture or not. One uses Support Vector Machines and the other uses Dynamic Time Warping method. We also introduced the appropriate method to select validate data to train SVMs using correlation coefficients and cross-correlation functions. A series of experiments was carried out to confirm the performance of the proposed methods. From the results of the experiment, we confirmed that the effectiveness of the two methods.},
 pages = {227--236},
 title = {表面筋電位を用いた認証システム実現のための個人識別手法の提案},
 volume = {47},
 year = {2018},
 yomi = {クロギ, トキヨシ and ヤマバ, ヒサアキ and アブラダ, ケンタロウ and オカザキ, ナオノブ and クロギ, トキヨシ}
}