Volume 4, Issue 1, June 2017, Pages 18 - 21
Exercise classification using CNN with image frames produced from time-series motion data
Hajime Itoh, Naohiko Hanajima, Yohei Muraoka, Makoto Ohata, Masato Mizukami, Yoshinori Fujihira
Available Online 1 June 2017.
- 10.2991/jrnal.2017.4.1.5How to use a DOI?
- CNN, Gray scale image, Exercises classification, Time-series data.
Exercise support systems for the elderly have been developed and some were equipped with a motion sensor to evaluate their exercise motion. Normally, it provides three-dimensional time-series data of over 20 joints. In this study, we propose to apply Convolutional Neural Network (CNN) methodology to the motion evaluation. The method converts the motion data of one exercise interval into one gray scale image. From simulation results, the CNN was possible to classify the images into specified motions.
- © 2013, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - JOUR AU - Hajime Itoh AU - Naohiko Hanajima AU - Yohei Muraoka AU - Makoto Ohata AU - Masato Mizukami AU - Yoshinori Fujihira PY - 2017 DA - 2017/06/01 TI - Exercise classification using CNN with image frames produced from time-series motion data JO - Journal of Robotics, Networking and Artificial Life SP - 18 EP - 21 VL - 4 IS - 1 SN - 2352-6386 UR - https://doi.org/10.2991/jrnal.2017.4.1.5 DO - 10.2991/jrnal.2017.4.1.5 ID - Itoh2017 ER -