Improved One-Dimensional Convolutional Neural Networks for Human Motion Recognition

被引：1

作者：

Wang, Shengzhi ^{[1
]}

Xiao, Shuo ^{[1
]}

Huang, Zhenzhen ^{[2
]}

Xu, Zhiou ^{[3
]}

Chen, Wei ^{[3
]}

机构：

[1] China Univ Min & Technol, Sch Comp Sci & Technol, Minist Educ, Engn Res Ctr Mine Digitalizat, Xuzhou, Jiangsu, Peoples R China

[2] China Univ Min & Technol, Sch Comp Sci & Technol, Lib, Xuzhou, Jiangsu, Peoples R China

[3] China Univ Min & Technol, Sch Comp Sci & Technol, Xuzhou, Jiangsu, Peoples R China

来源：

2020 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE | 2020年

基金：

中国国家自然科学基金;

关键词：

motion recognition; 1D-CNNs; wearable device; sample autonomous learning;

D O I：

10.1109/BIBM49941.2020.9313296

中图分类号：

Q5 [生物化学];

学科分类号：

071010 ; 081704 ;

摘要：

Wearable devices provide an extremely convenient way to collect a large amount of human motion data. In this paper, the human motion recognition method based on wearable devices is studied. We smooth the data to remove the noise caused by additional motion first. After that, the characteristic values that can distinguish the types of activities can be extracted. Then, we propose a human motion recognition method based on the improved one-dimensional convolutional neural networks(1D-CNNs). Compared with other traditional classification and recognition methods, the recognition rates of 11 human motions have been greatly improved. The average accuracy of each activity identification can reach 92.8%, while the average precision and recall can reach 98.7% and 92.8%.

引用

页码：2544 / 2547

页数：4

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