基于肌电信号和加速度信号的动态手势识别方法

doi:10.11772/j.issn.1001-9081.2017.09.2700

计算机应用 ›› 2017, Vol. 37 ›› Issue (9): 2700-2704.DOI: 10.11772/j.issn.1001-9081.2017.09.2700

• 应用前沿、交叉与综合 • 上一篇下一篇

基于肌电信号和加速度信号的动态手势识别方法

谢小雨, 刘喆颉

太原理工大学物理与光电工程学院, 太原 030024

收稿日期:2017-03-29 修回日期:2017-05-24 发布日期:2017-09-13 出版日期:2017-09-10
通讯作者: 刘喆颉,liuzhejie@tyut.edu.cn
作者简介:谢小雨(1992-),女,湖北荆州人,硕士研究生,主要研究方向:手势识别、模式识别、机器学习、微磁传感器;刘喆颉(1959-),男,江苏南京人,教授,博士,主要研究方向:磁存储、数据挖掘。
基金资助:
国家自然科学基金资助项目（61274089）；山西省国际合作项目（2014081029-2）。

Dynamic gesture recognition method based on EMG and ACC signal

XIE Xiaoyu, LIU Zhejie

College of Physics and Optoelectronics, Taiyuan University of Technology, Taiyuan Shanxi 030024, China

Received:2017-03-29 Revised:2017-05-24 Online:2017-09-13 Published:2017-09-10
Supported by:
This work is partially supported by National Natural Science Foundation of China (61274089),the International Cooperation Projects in Shanxi Province (2014081029-2).

摘要/Abstract

摘要： 为了增强手势识别的多样性和简便性，提出了一种基于肌电信号（EMG）和加速度（ACC）信息融合的方法来识别动态手势。首先，利用MYO传感器采集EMG和ACC的手势动作信息；然后分别对ACC和EMG信号作特征降维和预处理；最后，为减少训练样本数，提出用协作稀疏表示分类器来识别基于ACC信号的姿态手势，用动态时间规整（DTW）算法和K-最邻近分类器（KNN）来分类EMG信号的手形手势。其中在利用协作稀疏表示分类器识别ACC姿态信号时，通过对创建字典最佳样本个数以及特征降维的维数进行研究来降低手势识别的复杂度。实验结果表明，手形手势的平均识别率达到了99.17%，对于向上向下、向左向右4种姿态手势平均识别率达到 96.88%，而且计算速度快；对于总体的12个动态手势，其平均识别率达到96.11%。该方法对动态手势的识别率较高，计算速度快。

关键词: 手势识别, 协作稀疏表示, 肌电信号, 动态时间规整算法, 加速度

Abstract: To enhance the diversity and simplicity of hand gesture recognition, an approach based on ElectroMyoGraphy (EMG) and ACCeleration(ACC) signals was proposed to recognize dynamic gestures. Firstly, the gesture related information was collected by MYO sensors. Then, the dimensionality of ACC signal was reduced and the preprocessing of EMG was done. Finally, to reduce the number of training samples,the posture based on ACC signal was recognized by using Collaborative Sparse Representation (CSR) and the gesture based on EMG signal was classified by using Dynamic Time Warping (DTW) algorithm and the K-Nearest Neighbor (KNN) Classifier. When the ACC signal was identified by using CSR, the optimal number of samples and the dimensions of the dimensionality reduction were studied to reduce the complexity of gesture recognition. The experimental results show that the average recognition accuracy of the EMG for the hand gesture tested reaches 99.17%; the ACC signal for four postures achieve 96.88%. The recognition accuracy for the 12 dynamic gestures reaches 96.11%. This method has high recognition accuracy and fast calculation speed for dynamic gestures.

Key words: gesture recognition, Collaborative Sparse Representation (CSR), ElectroMyoGraphy (EMG), Dynamic Time Warping (DTW) algorithm, ACCeleration(ACC)

中图分类号:

TP391.4

谢小雨, 刘喆颉. 基于肌电信号和加速度信号的动态手势识别方法[J]. 计算机应用, 2017, 37(9): 2700-2704.

XIE Xiaoyu, LIU Zhejie. Dynamic gesture recognition method based on EMG and ACC signal[J]. Journal of Computer Applications, 2017, 37(9): 2700-2704.

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基于肌电信号和加速度信号的动态手势识别方法

Dynamic gesture recognition method based on EMG and ACC signal

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