GS6-4 Exercise classification using CNN with image frames produced from time-series motion data

Title:	GS6-4 Exercise classification using CNN with image frames produced from time-series motion data
Publication:	ICAROB2017
Volume:	22
Pages:	100-103
ISSN:	2188-7829
DOI:	10.5954/ICAROB.2017.GS6-4
Author(s):	Hajime Itoh, Naohiko Hanajima, Yohei Muraoka, Makoto Ohata, Masato Mizukami, Yoshinori Fujihira
Publication Date:	January 19, 2017
Keywords:	CNN, Gray scale image, Exercises classification, Time-series data
Abstract:	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.
PDF File:	https://alife-robotics.co.jp/members2017/icarob/data/html/data/GS_pdf/GS6/GS6-4.pdf
Copyright:	© The authors. This article is distributed under the terms of the Creative Commons Attribution License 4.0, which permits non-commercial use, distribution and reproduction in any medium, provided the original work is properly cited. See for details: https://creativecommons.org/licenses/by-nc/4.0/