Human-machine interaction control based on force myograph and electrical stimulation sensory feedback for multi-DOF robotic hand

被引:0
作者
Li, Nan [1 ]
Liu, Bo [1 ]
Huo, Hong [1 ]
Ye, Yuxuan [1 ]
Jiang, Li [2 ]
机构
[1] Beijing Aerospace Automatic Control Institute, Beijing
[2] State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin
来源
Jiqiren/Robot | 2015年 / 37卷 / 06期
关键词
Electrical stimulation; FMG (force myograph); Human-machine interaction; Robotic hand; Sensory feedback;
D O I
10.13973/j.cnki.robot.2015.0718
中图分类号
学科分类号
摘要
A wearable bi-directional human-machine interaction (HMI) system and its control methods are proposed to enable the user to control multi-DOF robotic hand freely and feel the gripping force from the robotic hand. A force sensory resistor (FSR) array is built to measure the forearm force myographic (FMG) signals corresponding to different hand motions of the user. A multiclass classifier is designed based on the support vector machine (SVM) algorithm to recognize the hand motions and generate motion codes to control the robotic hand movements. Moreover, sensory feedback is achieved by transforming the gripping force signals of the robotic hand into electrical stimulation signals of skin based on the principle of transcutaneous electrical nerve stimulation (TENS). Experimental results show that the motion mode recognition method based on FMG and SVM can identify 10 typical hand motions with the accuracy of above 95%. The electrical stimulation method can feed back the perception of gripping force to the body accurately and help the user to grip objects without vision. © 2015, Chinese Academy of Sciences. All right reserved.
引用
收藏
页码:718 / 724
页数:6
相关论文
共 21 条
[11]  
Yang D.P., Zhao J.D., Gu Y.K., Et al., An anthropomorphic robot hand developed based on underactuated mechanism and controlled by EMG signals, Journal of Bionic Engineering, 6, 3, pp. 255-263, (2009)
[12]  
Tenore F.V.G., Ramos A., Fahmy A., Et al., Decoding of individuated finger movements using surface electromyography, IEEE Transactions on Biomedical Engineering, 56, 5, pp. 1427-1434, (2009)
[13]  
Honda Y., Weber S., Lueth T.C., Intelligent recognition system for hand gestures, 3rd International IEEE/EMBS Conference on Neural Engineering, pp. 611-614, (2007)
[14]  
Fang H.G., Xie Z.W., Liu H., Et al., An exoskeleton force feedback master finger distinguishing contact and non-contact mode, IEEE/ASME International Conference on Advanced Intelligent Mechatronics, pp. 1059-1064, (2009)
[15]  
Yuan M., Myoelectric bionic control and tele-operation research, (2012)
[16]  
Mei X.H., The SEMG control of telerobot based on network and wireless, (2012)
[17]  
Blum J.E., Using force sensors to effectively control a belowelbow intelligent prosthetic device, (2012)
[18]  
Kaczmarek K.A., Webster J.G., Bachyrita P., Et al., Electrotactile and vibrotactile displays for sensory substitution systems, IEEE Transactions on Biomedical Engineering, 38, 1, pp. 1-16, (1991)
[19]  
Mcneal D.R., Analysis of a model for excitation of myelinated nerve, IEEE Transactions on Biomedical Engineering, 23, 4, pp. 329-337, (1976)
[20]  
Hsu C.W., Lin C.J., A comparison of methods for multiclass support vector machines, IEEE Transactions on Neural Networks, 13, 2, pp. 415-425, (2002)