Hand Gesture Recognition by Accelerometer-Based Cluster Dynamic Time Warping

被引:1
作者
王琳琳 [1 ]
夏侯士戟 [1 ]
机构
[1] School of Automation Engineering,University of Electronic Science and Technology of China
基金
国家重点研发计划;
关键词
main axis classification; sample clustering; dynamic time warping(DTW); gesture recognition;
D O I
10.19884/j.1672-5220.2017.04.013
中图分类号
TP391.41 [];
学科分类号
080203 ;
摘要
Aiming at the diversity of hand gesture traces by different people,the article presents novel method called cluster dynamic time warping( CDTW),which is based on the main axis classification and sample clustering of individuals. This method shows good performance on reducing the complexity of recognition and strong robustness of individuals. Data acquisition is implemented on a triaxial accelerometer with 100 Hz sampling frequency. A database of 2400 traces was created by ten subjects for the system testing and evaluation. The overall accuracy was found to be 98. 84% for user independent gesture recognition and 96. 7% for user dependent gesture recognition,higher than dynamic time warping( DTW),derivative DTW( DDTW) and piecewise DTW( PDTW) methods.Computation cost of CDTW in this project has been reduced 11 520 times compared with DTW.
引用
收藏
页码:551 / 555
页数:5
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