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

Hosono Satoshi, Shoji Nishimura, Ken Iwasaki, Emi Tamaki

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publicationSIGGRAPH Asia 2019 Posters, SA 2019
PublisherAssociation for Computing Machinery, Inc
ISBN (Electronic)9781450369435
DOIs
Publication statusPublished - 2019 Nov 17
EventSIGGRAPH Asia 2019 Posters - International Conference on Computer Graphics and Interactive Techniques, SA 2019 - Brisbane, Australia
Duration: 2019 Nov 172019 Nov 20

Publication series

NameSIGGRAPH Asia 2019 Posters, SA 2019

Conference

ConferenceSIGGRAPH Asia 2019 Posters - International Conference on Computer Graphics and Interactive Techniques, SA 2019
CountryAustralia
CityBrisbane
Period19/11/1719/11/20

Keywords

  • Muscle
  • Photosensor
  • Weight feeling

ASJC Scopus subject areas

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

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    Satoshi, H., Nishimura, S., Iwasaki, K., & Tamaki, E. (2019). Pondushand: Measure user’s weight feeling by photo sensor array around forearm. In 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