Stetho Touch: Touch Action Recognition System by Deep Learning with Stethoscope Acoustic Sensing

• Item type: Journal(1)
• 公開日: 2022-10-15
• タイトル: Stetho Touch: Touch Action Recognition System by Deep Learning with Stethoscope Acoustic Sensing
• タイトル / 言語: en
• タイトル: Stetho Touch: Touch Action Recognition System by Deep Learning with Stethoscope Acoustic Sensing
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: [一般論文] machine learning, deep learning, CHI, acoustic sensing
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_6501
• 資源タイプ: journal article
• 著者所属: Graduate of Faculty of Engineering, Sophia University
• 著者所属: Graduate of Faculty of Engineering, Sophia University
• 著者所属: Graduate of Faculty of Engineering, Sophia University
• 著者所属(英): en
• 著者所属(英): Graduate of Faculty of Engineering, Sophia University
• 著者所属(英): en
• 著者所属(英): Graduate of Faculty of Engineering, Sophia University
• 著者所属(英): en
• 著者所属(英): Graduate of Faculty of Engineering, Sophia University
• 著者名: Nagisa, Masuda; Koichi, Furukawa; Ikuko, Eguchi Yairi
• 著者名(英): Nagisa, Masuda; Koichi, Furukawa; Ikuko, Eguchi Yairi
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: Developing a new IoT device input method that can reduce the burden on users has become an important issue. This paper proposed a system Stetho Touch that identifies touch actions using acoustic information obtained when a user's finger makes contact with a solid object. To investigate the method, we implemented a prototype of an acoustic sensing device consisting of a low-pressure melamine veneer table, a stethoscope, and an audio interface. The CNN-LSTM classification model of combining CNN and LSTM classified the five touch actions with accuracy 88.26%, f-score 87.26% in LOSO and accuracy 99.39, f-score 99.39 in 18-fold cross-validation. The contributions of this paper are the following; (1) proposed a touch action recognition method using acoustic information that is more natural and accurate than existing methods, (2) evaluated a touch action recognition method using Deep Learning that can be processed in real-time using acoustic time series raw data as input, and (3) proved the compensations for the user dependence of touch actions by providing a learning phase or performing sequential learning during use.; ------------------------------; This is a preprint of an article intended for publication Journal of; Information Processing(JIP). This preprint should not be cited. This; article should be cited as: Journal of Information Processing Vol.30(2022) (online); DOI http://dx.doi.org/10.2197/ipsjjip.30.718; ------------------------------
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: Developing a new IoT device input method that can reduce the burden on users has become an important issue. This paper proposed a system Stetho Touch that identifies touch actions using acoustic information obtained when a user's finger makes contact with a solid object. To investigate the method, we implemented a prototype of an acoustic sensing device consisting of a low-pressure melamine veneer table, a stethoscope, and an audio interface. The CNN-LSTM classification model of combining CNN and LSTM classified the five touch actions with accuracy 88.26%, f-score 87.26% in LOSO and accuracy 99.39, f-score 99.39 in 18-fold cross-validation. The contributions of this paper are the following; (1) proposed a touch action recognition method using acoustic information that is more natural and accurate than existing methods, (2) evaluated a touch action recognition method using Deep Learning that can be processed in real-time using acoustic time series raw data as input, and (3) proved the compensations for the user dependence of touch actions by providing a learning phase or performing sequential learning during use.; ------------------------------; This is a preprint of an article intended for publication Journal of; Information Processing(JIP). This preprint should not be cited. This; article should be cited as: Journal of Information Processing Vol.30(2022) (online); DOI http://dx.doi.org/10.2197/ipsjjip.30.718; ------------------------------
• 書誌レコードID / 収録物識別子タイプ: NCID
• 書誌レコードID / 収録物識別子: AN00116647
• 書誌情報: 情報処理学会論文誌; 巻 63, 号 10, 発行日 2022-10-15
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 1882-7764
• 公開者 / 言語: ja
• 公開者 / 出版者: 情報処理学会