Journal article
Machine Learning-driven Classification of Hand Motion for the 3D-proximity-sensors Unit
IEEJ JOURNAL OF INDUSTRY APPLICATIONS, Vol.13(2), pp.165-170
01/03/2024
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Abstract
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.
Details
- Title
- Machine Learning-driven Classification of Hand Motion for the 3D-proximity-sensors Unit
- Creators
- Tomoaki Kashiwao - Kindai UniversityKeita Hayashi - Kindai UniversityMasayuki Hiro - Kindai UniversityRyoya Ogino - Kindai UniversityMikio Deguchi - National Institute of Technology, Akashi College
- Publication Details
- IEEJ JOURNAL OF INDUSTRY APPLICATIONS, Vol.13(2), pp.165-170
- Publisher
- Inst Electrical Engineers Japan
- Number of pages
- 6
- Identifiers
- 991013304504602368
- Copyright
- © 2024 The Institute of Electrical Engineers of Japan.
- Academic Unit
- Faculty of Science and Engineering
- Language
- English
- Resource Type
- Journal article