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

DOI

10.2991/jrnal.2017.4.1.5How to use a DOI?

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.

Copyright

© 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/).

Download article (PDF)

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  -