Presentation | 2013-08-01 Machine Learning-based Hand Gesture Identification using Doppler Sensor Junya Oikawa, Eiji Kamioka, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | With the spread of electronic devices, the operation method for controlling the devices has been diversified. Gesture-based interface, which is a user-friendly and intuitive input operation, is also one of them. Detection of user's gesture using a camera or an accelerometer has already been put into practice. However, they are not versatile since there are some constraints such as production cost, privacy issue and sensor's fixing point. This study proposes a gesture identification method using multiple Doppler sensors. Concretely, hand gestures are detected by the Doppler sensors, and then each movement direction of the hand is identified. In this paper, the effectiveness of the proposed method will be stated through some experiments. In addition, the possibility of versatile use of it will be discussed. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | Doppler Sensor / Gesture Identification / Machine Learning / Artificial Neural Network |
Paper # | MoNA2013-16 |
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Committee | MoNA |
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Conference Date | 2013/7/25(1days) |
Place (in Japanese) | (See Japanese page) |
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Paper Information | |
Registration To | Mobile Network and Applications(MoNA) |
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Language | JPN |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | Machine Learning-based Hand Gesture Identification using Doppler Sensor |
Sub Title (in English) | |
Keyword(1) | Doppler Sensor |
Keyword(2) | Gesture Identification |
Keyword(3) | Machine Learning |
Keyword(4) | Artificial Neural Network |
1st Author's Name | Junya Oikawa |
1st Author's Affiliation | Graduate School of Engineering and Science, Shibaura Institute of Technology() |
2nd Author's Name | Eiji Kamioka |
2nd Author's Affiliation | Graduate School of Engineering and Science, Shibaura Institute of Technology |
Date | 2013-08-01 |
Paper # | MoNA2013-16 |
Volume (vol) | vol.113 |
Number (no) | 168 |
Page | pp.pp.- |
#Pages | 6 |
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