Stetho Touch: Touch Action Recognition System by Deep Learning with Stethoscope Acoustic Sensing
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- Masuda Nagisa
- Graduate of Faculty of Engineering, Sophia University
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- Furukawa Koichi
- Graduate of Faculty of Engineering, Sophia University
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- Yairi Ikuko Eguchi
- Graduate of Faculty of Engineering, Sophia University
説明
<p>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.</p>
収録刊行物
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- Journal of Information Processing
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Journal of Information Processing 30 (0), 718-728, 2022
一般社団法人 情報処理学会
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詳細情報 詳細情報について
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- CRID
- 1390856738294568960
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- ISSN
- 18826652
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- 本文言語コード
- en
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- データソース種別
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- JaLC
- Crossref
- KAKEN
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- 抄録ライセンスフラグ
- 使用不可