Machine Learning-driven Classification of Hand Motion for the 3D-proximity-sensors Unit

カテゴリ: 論文誌(論文単位)

グループ名: 【D】産業応用部門（英文）

発行日: 2024/03/01

タイトル(英語): Machine Learning-driven Classification of Hand Motion for the 3D-proximity-sensors Unit

著者名: Tomoaki Kashiwao (Graduate School of Science and Engineering Kindai University), Keita Hayashi (Graduate School of Science and Engineering Kindai University), Masayuki Hiro (Graduate School of Science and Engineering Kindai University), Ryoya Ogino (Graduate School of Science and Engineering Kindai University), Mikio Deguchi (National Institute of Technology, Akashi College)

著者名(英語): Tomoaki Kashiwao (Graduate School of Science and Engineering Kindai University), Keita Hayashi (Graduate School of Science and Engineering Kindai University), Masayuki Hiro (Graduate School of Science and Engineering Kindai University), Ryoya Ogino (Graduate School of Science and Engineering Kindai University), Mikio Deguchi (National Institute of Technology, Akashi College)

キーワード: capacitive proximity sensor，motion sensor，machine learning，classification，random forest

要約(英語): This paper proposes a machine learning-based method to identify human hand motion using a 3D capacitive proximity sensor based on multiple sensing electrodes, which was developed in our previous studies. Although the sensor can detect nearby objects, determining their position and motion directly from the nonlinear outputs of the sensor is difficult. This study proposes a random forest method to identify the direction of movement of a human hand passing above the 3D proximity sensor unit. The time-series data obtained by combining the outputs of three channels are classified into four directions: upward, downward, rightward, and leftward. Experimental evaluation reveals that the proposed method achieves over 95% classification accuracy in all four directions.

本誌: IEEJ Journal of Industry Applications Vol.13 No.2 （2024） Special Issue on “Motion Control and its Related Technologies”

本誌掲載ページ: 165-170 p

原稿種別: 論文／英語

電子版へのリンク: https://www.jstage.jst.go.jp/article/ieejjia/13/2/13_23004858/_article/-char/ja/