Pondushand: Measure user’s weight feeling by photo sensor array around forearm

Hosono Satoshi, Shoji Nishimura, Ken Iwasaki, Emi Tamaki

研究成果: Conference contribution

抜粋

Weight feeling is important for musical instrument training and physical workout training. But it is difficult to convey accurate information about weight feeling to trainer with visual or verbal information.This study measures weight feeling on muscles when playing a piano keyboard or doing push-ups using a wearable device. To do that, the muscle deformation data is measured by a photo-sensor array wrapped around the forearm. This data is input to a trained Support Vector Regression (SVR) classifier that estimates weight feeling as output. As a result of our experiment, the correlation coefficient between the measured value and the estimated value was 0.911 while RMSE and MAE were 236 g and150 g respectively when estimating weights up to 2000 g. In future work, we want to use this technique under many arm posture.

元の言語English
ホスト出版物のタイトルSIGGRAPH Asia 2019 Posters, SA 2019
出版者Association for Computing Machinery, Inc
ISBN(電子版)9781450369435
DOI
出版物ステータスPublished - 2019 11 17
イベントSIGGRAPH Asia 2019 Posters - International Conference on Computer Graphics and Interactive Techniques, SA 2019 - Brisbane, Australia
継続期間: 2019 11 172019 11 20

出版物シリーズ

名前SIGGRAPH Asia 2019 Posters, SA 2019

Conference

ConferenceSIGGRAPH Asia 2019 Posters - International Conference on Computer Graphics and Interactive Techniques, SA 2019
Australia
Brisbane
期間19/11/1719/11/20

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Software
  • Computer Vision and Pattern Recognition

フィンガープリント Pondushand: Measure user’s weight feeling by photo sensor array around forearm' の研究トピックを掘り下げます。これらはともに一意のフィンガープリントを構成します。

  • これを引用

    Satoshi, H., Nishimura, S., Iwasaki, K., & Tamaki, E. (2019). Pondushand: Measure user’s weight feeling by photo sensor array around forearm. : SIGGRAPH Asia 2019 Posters, SA 2019 [3364552] (SIGGRAPH Asia 2019 Posters, SA 2019). Association for Computing Machinery, Inc. https://doi.org/10.1145/3355056.3364552