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Grand Ballroom
Estimation and Presentation of Racket Grip Vibration with Tennis Video
Kentaro Yoshida, Yuuki Horiuchi, Tomohiro Ichiyama, Seki Inoue, Yasutoshi Makino, and Hiroyuki Shinoda
(University of Tokyo, Japan)
Demo 5
Publisher's Version
Picture (Local)
Abstract: In this study, vibrotactile perception of a person in a video is estimated from the visual and audio information by using neural network. In the estimation, we limit the video scene to the back view of a tennis player rallying. But, our method can work regardless of the difference in some conditions such as locations, a player’s clothes, and sound environments. We use tennis videos taken in three locations for neural network learning of the relation between the video and measured acceleration of the racket grip. Then we show the grip sensation can be successfully estimated from an unknown video which was taken in a different location from learning. The quality of the produced vibrotactile sensation is also evaluated by a subject experiment. In the demonstration, user can experience the estimated (or real) vibrotactile sensation with our device imitating a tennis racket grip while watching the corresponding video.


Time stamp: 2019-06-18T22:57:07+02:00