@article{oai:muroran-it.repo.nii.ac.jp:00009694,
 author = {ITOH, Hajime and HANAJIMA,  Naohiko and 花島, 直彦 and MURAOKA, Yohei and OHATA, Makoto and 水上, 雅人 and MIZUKAMI, Masato and FUJIHIRA, Yoshinori and 藤平, 祥孝},
 issue = {1},
 journal = {Journal of Robotics, Networking and Artificial Life},
 month = {Jun},
 note = {application/pdf, 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.},
 pages = {18--21},
 title = {Exercise classification using CNN with image frames produced from time-series motion data},
 volume = {4},
 year = {2017},
 yomi = {ハナジマ, ナオヒコ and ミズカミ, マサト and フジヒラ, ヨシノリ}
}