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

被引：0

作者：

Itoh, Hajime ^{[1
]}

Hanajima, Naohiko ^{[2
]}

Muraoka, Yohei ^{[3
]}

Ohata, Makoto ^{[3
]}

Mizukami, Masato ^{[2
]}

Fujihira, Yoshinori ^{[2
]}

机构：

[1] Muroran Inst Technol, Div Prod Syst Engn, 27-1 Mizumoto Cho, Muroran, Hokkaido 0508585, Japan

[2] Muroran Inst Technol, Coll Design Mfg Technol, 27-1 Mizumoto Cho, Muroran, Hokkaido 0508585, Japan

[3] Steel Mem Muroran Hosp, 1-45 Chiribetsu Cho, Muroran, Hokkaido 0500076, Japan

来源：

ICAROB 2017: PROCEEDINGS OF THE 2017 INTERNATIONAL CONFERENCE ON ARTIFICIAL LIFE AND ROBOTICS | 2017年

关键词：

CNN; Gray scale image; Exercises classification; Time-series data;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：P100 / P103

页数：4

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