{"created":"2023-05-15T10:00:49.215925+00:00","id":5239,"links":{},"metadata":{"_buckets":{"deposit":"df4acdf7-eadf-4465-acf5-7140901bdd81"},"_deposit":{"created_by":5,"id":"5239","owner":"5","owners":[5],"pid":{"revision_id":0,"type":"depid","value":"5239"},"status":"published"},"_oai":{"id":"oai:miyazaki-u.repo.nii.ac.jp:00005239","sets":["73","73:36","73:36:330","73:36:330:327"]},"author_link":["11839","25736","28382","6484","14888"],"item_10002_alternative_title_1":{"attribute_name":"その他（別言語等）のタイトル","attribute_value_mlt":[{"subitem_alternative_title":"キン デンイ ニ ヨ ル コジン ニンショウ システム ジツゲン ノ タメ ノ キン デンパ カタ ノ トクチョウ ハカ ニ カン ス ル ケントウ"}]},"item_10002_biblio_info_7":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicIssueDates":{"bibliographicIssueDate":"2017-07-31","bibliographicIssueDateType":"Issued"},"bibliographicPageEnd":"256","bibliographicPageStart":"251","bibliographicVolumeNumber":"46","bibliographic_titles":[{"bibliographic_title":"宮崎大学工学部紀要","bibliographic_titleLang":"ja"},{"bibliographic_title":"Memoirs of Faculty of Engineering, University of Miyazaki","bibliographic_titleLang":"en"}]}]},"item_10002_description_5":{"attribute_name":"抄録","attribute_value_mlt":[{"subitem_description":"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 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, are detected over the skin surface. Muscle movement can be differentiated by analyzing the s-EMG. We proposed a method that uses a list of gestures as a password in the previous study. In this paper, results of experiments are presented that was carried out to investigate the performance of the method identifying gestures from s-EMG signals using support vector machines (SVM). An experiment to identify users from s-EMG signals was carried out at the same time. The performance of SVM as a classifier of our method was also discussed according to the results.","subitem_description_language":"en","subitem_description_type":"Abstract"}]},"item_10002_publisher_8":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"宮崎大学工学部","subitem_publisher_language":"ja"},{"subitem_publisher":"Faculty of Engineering, University of 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