Presentation 2013-08-01
Machine Learning-based Hand Gesture Identification using Doppler Sensor
Junya Oikawa, Eiji Kamioka,
PDF Download Page PDF download Page Link
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
Date of Issue

Conference Information
Committee MoNA
Conference Date 2013/7/25(1days)
Place (in Japanese) (See Japanese page)
Place (in English)
Topics (in Japanese) (See Japanese page)
Topics (in English)
Chair
Vice Chair
Secretary
Assistant

Paper Information
Registration To Mobile Network and Applications(MoNA)
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
Date of Issue