[1]于海龙,范雪莉,宫海兰,等.一种基于sEMG信号的手势识别方法研究[J].江西师范大学学报(自然科学版),2018,(05):512-517.[doi:10.16357/j.cnki.issn1000-5862.2018.05.14]
　YU Hailong,FAN Xueli,GONG Hailan,et al.The Study of Hand Gesture Recognition Method Based on sEMG Signal[J].Journal of Jiangxi Normal University:Natural Science Edition,2018,(05):512-517.[doi:10.16357/j.cnki.issn1000-5862.2018.05.14]
点击复制

一种基于sEMG信号的手势识别方法研究()
分享到：

《江西师范大学学报》（自然科学版）[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2018年05期
页码:
512-517
栏目:
虚拟现实技术
出版日期:
2018-10-20

文章信息/Info

Title:
The Study of Hand Gesture Recognition Method Based on sEMG Signal
文章编号:
1000-5862(2018)05-0512-06
作者:
于海龙1; 范雪莉1; 宫海兰2; 谢 叻3; 4
1.许昌学院电气(机电)工程学院,河南 许昌 461000; 2.上海信息技术学校,上海 200331; 3.上海交通大学国家数字化制造技术中心,上海 200030; 4.上海交通大学生物医学工程学院,上海 200030
Author(s):
YU Hailong1; FAN Xueli1; GONG Hailan2; XIE Le3; 4
1.School of Electrical Engineering and Mechano-Electronic Engineering,Xuchang University,Xuchang Henan 461000,China; 2.Shanghai Information Technology College,Shanghai 200331,China; 3.Natinoal Digital Manufacturing Technology Center,Shanghai Jiaotong Un
关键词:
表面肌电信号;  模式识别;  BP神经网络;  端点检测
Keywords:
sEMG signal;  mode recognition;  BP neural network;  endpoint detection
分类号:
TP 391.9
DOI:
10.16357/j.cnki.issn1000-5862.2018.05.14
文献标志码:
A
摘要:
随着机器人技术的发展,利用表面肌电(surface electromyography,sEMG)信号进行动作识别成为研究的热点.针对sEMG与手部动作关系复杂且实际应用困难的问题,该文提出一种基于BP(back propagation)神经网络的模式识别系统,可通过指浅屈肌和肱挠肌的2路sEMG信息源,识别手部6种不同姿态.该研究采用1阶数字低通无限脉冲响应滤波器提取信号包络,并利用能量特征值进行端点检测,选取短时能量、过零率和12阶线性预测系数进行模式识别.实验结果表明:该方法可以达到90%以上的识别正确率,具有一定的实际应用前景.
Abstract:
The research of gesture recognition based on sEMG is becoming a hot spot in recent years with the development of robotics.In view of the complex relationship between sEMG and hand gestures and the difficulty of practical application,a pattern recognition system based on BP(back propagation)neural network is proposed which can recognize six hand gestures by the sEMG of superficial digital muscle and flex muscle.The signal envelope is extract by the first-order infinite impulse response digital low-pass filter.And the energy eigenvalues are chosen to do endpoint detection,short-time energy and zero crossing rates and 12 level linear prediction coefficient are adopted to do pattern recognition.Finally,a pattern recognition experiment has been done which recearchs the relationship between the sEMG and six different hand gestures and the accuracy is above 90%.The result shows that the method proposed in this study can achieve a high recognition rate and has a practical application prospect.

参考文献/References:

[1] Lambelet C,Lyu M,Woolley D,et al.The eWrist-A wearable wrist exoskeleton with sEMG-based force control for stroke rehabilitation[C]∥Rehabilitation Robotics(ICORR),2017 International Conference on IEEE,2017:726-733.
[2] Meng Qingyun,Meng Qiaoling,Yu Hongliu.A survey on sEMG control strategies of wearable handexoskeleton for rehabilitation[C]∥Intelligent Robot Systems(ACIRS),2017 2nd Asia-Pacific Conference on IEEE,2017:165-169.
[3] Wu Jian,Tian Zhongjun,Sun Lu.Real-time American sign language recognition using wrist-worn motion and surface EMG sensors[C]∥Wearable and Implantable Body Sensor Networks(BSN),2015 IEEE 12th International Conference on IEEE,2015:1-6.
[4] Kim J,Kim E,Park S,et al.Implementation of a sign language primitive framework using EMG and motion sensors[C]∥Consumer Electronics,2016 IEEE 5th Global Conference on IEEE,2016:1-2.
[5] Su Ruiliang,Chen Xiang,Cao Shuai,et al.Random forest-based recognition of isolated sign language subwords using data from accelerometers and surface electromyographicsensors[J].Sensors,2016,16(1):100.
[6] Sun Guanghao,Yu Wenwei.Development of an EMG-based human-machine interface on open-source linux platform for evaluating the motor skill acquisition process[C]∥The 17th International Conference on Biomedical Engineering,Springer,Singapore,2017:38-42.
[7] Huang Youjia,Liu Honghai.Performances of surface EMG and ultrasound signals in recognizing finger motion[C]∥Human System Interactions(HSI),2016 9th International Conference on IEEE,2016:117-122.
[8] Lu Zhiyuan,Chen Xiang,Li Qiang.A hand gesture recognition framework and wearable gesture-based interaction prototype for mobile devices[J].IEEE Transactions on Human-Machine Systems,2014,44(2):293-299.
[9] Benatti S,Milosevic B,Casamassima F,et al.EMG-based hand gesture recognition with flexible analog front end[C]∥Biomedical Circuits and Systems Conference(BioCAS),2014 IEEE IEEE,2014:57-60.
[10] Ju Zhaojie,Liu Honghai.Human hand motion analysis with multisensory information[M].Berlin:Human Motion Sensing and Recognition,Springer Berlin Heidelberg,2017:171-191.
[11] Rossi M,Benatti S,Farella E,et al.Hybrid EMG classifier based on HMM and SVM for hand gesture recognition in prosthetics[C]∥Industrial Technology(ICIT),2015 IEEE International Conference on IEEE,2015:1700-1705.
[12] Palermo F,Cognolato M,Gijsberts A,et al.Repeatability of grasp recognition for robotic hand prosthesis control based on sEMG data[C]∥Rehabilitation Robotics(ICORR),2017 International Conference on IEEE,2017:1154-1159.
[13] Raj R,Sivanandan K S.Comparative study on estimation of elbow kinematics based on EMG time domain parameters using neural network and ANFIS NARX model[J].Journal of Intelligent and Fuzzy Systems,2017,32(1):791-805.
[14] Reaz M B I,Hussain M S,Mohd-Yasin F.Techniques of EMG signal analysis:detection,processing,classification and applications[J].Biological Procedures Online,2006,8(1):11-35.
[15] Mahapatra S,Mohanta D,Mohanty P K,et al.Classification of EMG signals using ANFIS for the detection of neuromuscular disorders[M].Singapore:Recent Developments in Intelligent Computing,Communication and Devices,Springer,2017:53-60.

备注/Memo

备注/Memo:
收稿日期:2018-06-01
基金项目:国家自然科学基金面上(61672341)资助项目.
作者简介:于海龙(1984-),男,黑龙江绥化人,讲师,博士,主要从事医疗机器人控制和生物信号处理的研究.E-mail:yuhailonglong@hotmail.com
更新日期/Last Update: 2018-10-20