[1] Goodrich M A, Schultz A C. Human-robot interaction:a survey. Foundations and Trends in Human-Computer Interaction, 2007, 1(3):203-275 doi: 10.1561/1100000005
[2] 胡进, 侯增广, 陈翼雄, 张峰, 王卫群.下肢康复机器人及其交互控制方法.自动化学报, 2014, 40(11):2377-2390 http://www.aas.net.cn/CN/abstract/abstract18514.shtml

Hu Jin, Hou Zeng-Guang, Chen Yi-Xiong, Zhang Feng, Wang Wei-Qun. Lower limb rehabilitation robots and interactive control methods. Acta Automatica Sinica, 2014, 40(11):2377-2390 http://www.aas.net.cn/CN/abstract/abstract18514.shtml
[3] Nam Y, Koo B, Cichocki A, Choi S. GOM-face:GKP, EOG, and EMG-based multimodal interface with application to humanoid robot control. IEEE Transactions on Biomedical Engineering, 2014, 61(2):453-462 doi: 10.1109/TBME.2013.2280900
[4] Artemiadis P. EMG-based robot control interfaces:past, present and future. Advances in Robotics & Automation, 2012, 1(2):1-3
[5] Ngeo J G, Tamei T, Shibata T. Continuous and simultaneous estimation of finger kinematics using inputs from an EMG-to-muscle activation model. Journal of NeuroEngineering and Rehabilitation, 2014, 11:122 doi: 10.1186/1743-0003-11-122
[6] 佟丽娜, 侯增广, 彭亮, 王卫群, 陈翼雄, 谭民.基于多路sEMG时序分析的人体运动模式识别方法.自动化学报, 2014, 40(5):810-821 http://www.aas.net.cn/CN/abstract/abstract18349.shtml

Tong Li-Na, Hou Zeng-Guang, Peng Liang, Wang Wei-Qun, Chen Yi-Xiong, Tan Min. Multi-channel sEMG time series analysis based human motion recognition method. Acta Automatica Sinica, 2014, 40(5):810-821 http://www.aas.net.cn/CN/abstract/abstract18349.shtml
[7] Chowdhury R H, Reaz M B I, Ali M A B, Bakar A A A, Chellappan K, Chang T G. Surface electromyography signal processing and classification techniques. Sensors, 2013, 13(9):12431-12466 doi: 10.3390/s130912431
[8] Ahsan M R, Ibrahimy M I, Khalifa O O. EMG signal classification for human computer interaction:a review. European Journal of Scientific Research, 2009, 33(3):480-501 http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.468.4287&rep=rep1&type=pdf
[9] 丁其川, 赵新刚, 韩建达.基于肌电信号的上肢多关节连续运动估计.机器人, 2014, 36(4):469-476 http://www.cnki.com.cn/Article/CJFDTOTAL-JQRR201404012.htm

Ding Qi-Chuan, Zhao Xin-Gang, Han Jian-Da. EMG-based estimation for multi-joint continuous movement of human upper limb. Robot, 2014, 36(4):469-476 http://www.cnki.com.cn/Article/CJFDTOTAL-JQRR201404012.htm
[10] Ison M, Artemiadis P. Multi-directional impedance control with electromyography for compliant human-robot interaction. In:Proceedings of the 2015 International Conference on Rehabilitation Robotics (ICORR). Singapore:IEEE, 2015. 416-421
[11] Farina D, Merletti R, Enoka R M. The extraction of neural strategies from the surface EMG. Journal of Applied Physiology, 2004, 96(4):1486-1495 doi: 10.1152/japplphysiol.01070.2003
[12] De Luca C J. Imaging the Behavior of Motor Units by Decomposition of the EMG Signal. Boston, MA, USA:Delsys Inc., 2008.
[13] Chu J U, Moon I, Lee Y J, Kim S K, Mun M S. A supervised feature-projection-based real-time EMG pattern recognition for multifunction myoelectric hand control. IEEE/ASME Transactions on Mechatronics, 2007, 12(3):282-290 doi: 10.1109/TMECH.2007.897262
[14] Scheme E J, Englehart K B, Hudgins B S. Selective classification for improved robustness of myoelectric control under nonideal conditions. IEEE Transactions on Biomedical Engineering, 2011, 58(6):1698-1705 doi: 10.1109/TBME.2011.2113182
[15] Jamal M Z. Signal acquisition using surface EMG and circuit design considerations for robotic prosthesis. Computational Intelligence in Electromyography Analysis--A Perspective on Current Applications and Future Challenges. Rijeka:INTECH Open Access Publisher, 2012.
[16] Li Z J, Wang B C, Sun F C, Yang C G, Xie Q, Zhang W D. sEMG-based joint force control for an upper-limb power-assist exoskeleton robot. IEEE Journal of Biomedical and Health Informatics, 2014, 18(3):1043-1050 doi: 10.1109/JBHI.2013.2286455
[17] Pons J L. Wearable Robots:Biomechatronic Exoskeletons. West Sussex:John Wiley & Sons Ltd, 2008. 87-122
[18] Gopura R A R C, Bandara D S V, Gunasekara J M P, Jayawardane T S S. Recent trends in EMG-based control methods for assistive robots. Electrodiagnosis in New Frontiers of Clinical Research, 2013:237-268 https://www.researchgate.net/publication/236981286_Recent_Trends_in_EMG-Based_Control_Methods_for_Assistive_Robots
[19] Gijsberts A, Atzori M, Castellini C, Muller H, Caputo B. Movement error rate for evaluation of machine learning methods for sEMG-based hand movement classification. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2014, 22(4):735-744 doi: 10.1109/TNSRE.2014.2303394
[20] Castellini C, Arquer A, Artigas J. sEMG-based estimation of human stiffness:towards impedance-controlled rehabilitation. In:Proceedings of the 5th International Conference on Biomedical Robotics and Biomechatronics. Sao Paulo, Brazil:IEEE, 2014. 604-609
[21] Zhang D H, Zhao X G, Han J D, Zhao Y W. A comparative study on PCA and LDA based EMG pattern recognition for anthropomorphic robotic hand. In:Proceedings of the 2014 International Conference on Robotics and Automation (ICRA). Hong Kong, China:IEEE, 2014. 4850-4855
[22] 李阳, 田彦涛, 陈万忠.基于FFT盲辨识的肌电信号建模及模式识别.自动化学报, 2012, 38(1):128-134 doi: 10.3724/SP.J.1004.2012.00128

Li Yang, Tian Yan-Tao, Chen Wan-Zhong. Modeling and classifying of sEMG based on FFT blind identification. Acta Automatica Sinica, 2012, 38(1):128-134 doi: 10.3724/SP.J.1004.2012.00128
[23] Englehart K, Hudgins B, Parker P A. A wavelet-based continuous classification scheme for multifunction myoelectric control. IEEE Transactions on Biomedical Engineering, 2001, 48(3):302-311 doi: 10.1109/10.914793
[24] Peleg D, Braiman E, Yom-Tov E, Inbar G F. Classification of finger activation for use in a robotic prosthesis arm. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2002, 10(4):290-293 doi: 10.1109/TNSRE.2002.806831
[25] Khezri M, Jahed M. A neuro-fuzzy inference system for sEMG-based identification of hand motion commands. IEEE Transactions on Industrial Electronics, 2011, 58(5):1952-1960 doi: 10.1109/TIE.2010.2053334
[26] Huang Y, Englehart K B, Hudgins B, Chan A D C. A Gaussian mixture model based classification scheme for myoelectric control of powered upper limb prostheses. IEEE Transactions on Biomedical Engineering, 2005, 52(11):1801-1811 doi: 10.1109/TBME.2005.856295
[27] Matsubara T, Morimoto J. Bilinear modeling of EMG signals to extract user-independent features for multiuser myoelectric interface. IEEE Transactions on Biomedical Engineering, 2013, 60(8):2205-2213 doi: 10.1109/TBME.2013.2250502
[28] Chan A D C, Englehart K B. Continuous myoelectric control for powered prostheses using hidden Markov models. IEEE Transactions on Biomedical Engineering, 2005, 52(1):121-124 doi: 10.1109/TBME.2004.836492
[29] Li Z, Wang B, Yang C, Xie Q. Boosting-based EMG patterns classification scheme for robustness enhancement. IEEE Journal of Biomedical and Health Informatics, 2013, 17(3):545-552 doi: 10.1109/JBHI.2013.2256920
[30] Amsüss S, Goebel P M, Jiang N, Graimann B, Paredes L, Farina D. Self-correcting pattern recognition system of surface EMG signals for upper limb prosthesis control. IEEE Transactions on Biomedical Engineering, 2014, 61(4):1167-1176 doi: 10.1109/TBME.2013.2296274
[31] Li G L, Schultz A E, Kuiken T A. Quantifying pattern recognition-based myoelectric control of multifunctional transradial prostheses. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2010, 18(2):185-192 doi: 10.1109/TNSRE.2009.2039619
[32] Nan B, Okamoto M, Tsuji T. A hybrid motion classification approach for EMG-based human-robot interfaces using Bayesian and neural networks. IEEE Transactions on Robotics, 2009, 25(3):502-511 doi: 10.1109/TRO.2009.2019782
[33] Young A J, Smith L H, Rouse E J, Hargrove L J. Classification of simultaneous movements using surface EMG pattern recognition. IEEE Transactions on Biomedical Engineering, 2013, 60(5):1250-1258 doi: 10.1109/TBME.2012.2232293
[34] Momen K, Krishnan S, Chau T. Real-time classification of forearm electromyographic signals corresponding to user-selected intentional movements for multifunction prosthesis control. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2007, 15(4):535-542 doi: 10.1109/TNSRE.2007.908376
[35] Liu J, Zhou P. A novel myoelectric pattern recognition strategy for hand function restoration after incomplete cervical spinal cord injury. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2013, 21(1):96-103 doi: 10.1109/TNSRE.2012.2218832
[36] Kiguchi K, Tanaka T, Fukuda T. Neuro-fuzzy control of a robotic exoskeleton with EMG signals. IEEE Transactions on Fuzzy Systems, 2004, 12(4):481-490 doi: 10.1109/TFUZZ.2004.832525
[37] Gopura R A R C, Kiguchi K, Li Y. SUEFUL-7:a 7DOF upper-limb exoskeleton robot with muscle-model-oriented EMG-based control. In:Proceedings of the 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems. St. Louis, USA:IEEE, 2009. 1126-1131
[38] Ngeo J, Tamei T, Shibata T, Orlando M F F, Behera L, Saxena A. Control of an optimal finger exoskeleton based on continuous joint angle estimation from EMG signals. In:Proceedings of the 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Osaka, Japan:IEEE, 2013. 338-341
[39] Kwon S, Kim Y, Kim J. Movement stability analysis of surface electromyography-based elbow power assistance. IEEE Transactions on Biomedical Engineering, 2014, 61(4):1134-1142 doi: 10.1109/TBME.2013.2295381
[40] Duc D M, Kazuhiko T, Takanori M. EMG-moment model of human arm for rehabilitation robot system. In:Proceedings of the 10th International Conference on Control, Automation, Robotics and Vision. Hanoi, Vietnam:IEEE, 2008. 190-195
[41] Aung Y M, Al-Jumaily A. Estimation of upper limb joint angle using surface EMG signal. International Journal of Advanced Robotic Systems, 2013, 10(369):1-8 http://www.wenkuxiazai.com/doc/5bed974baaea998fcc220e9f.html
[42] Hayashibe M, Guiraud D, Poignet P. EMG-to-force estimation with full-scale physiology based muscle model. In:Proceedings of the 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems. St. Louis, USA:IEEE, 2009. 1621-1626
[43] Buchanan T S, Lloyd D G, Manal K, Besier T F. Neuromusculoskeletal modeling:estimation of muscle forces and joint moments and movements from measurements of neural command. Journal of Applied Biomechanics, 2004, 20(4):367-395 doi: 10.1123/jab.20.4.367
[44] Fleischer C. Controlling Exoskeletons with EMG Signals and a Biomechanical Body Model[Ph.D. dissertation], Technische University Berlin, Germany, 2007.
[45] Cavallaro E E, Rosen J, Perry J C, Burns S. Real-time myoprocessors for a neural controlled powered exoskeleton arm. IEEE Transactions on Biomedical Engineering, 2006, 53(11):2387-2396 doi: 10.1109/TBME.2006.880883
[46] Fleischer C, Hommel G. A human-exoskeleton interface utilizing electromyography. IEEE Transactions on Robotics, 2008, 24(4):872-882 doi: 10.1109/TRO.2008.926860
[47] Sartori M, Reggiani M, Farina D, Lloyd D G. EMG-driven forward-dynamic estimation of muscle force and joint moment about multiple degrees of freedom in the human lower extremity. PLoS One, 2012, 7(12):e52618 doi: 10.1371/journal.pone.0052618
[48] Han J D, Ding Q C, Xiong A B, Zhao X G. A state-space EMG model for the estimation of continuous joint movements. IEEE Transactions on Industrial Electronics, 2015, 62(7):4267-4275 doi: 10.1109/TIE.2014.2387337
[49] Cheron G, Leurs F, Bengoetxea A, Draye J P, Destrée M, B Dan. A dynamic recurrent neural network for multiple muscles electromyographic mapping to elevation angles of the lower limb in human locomotion. Journal of Neuroscience Methods, 2003, 129(2):95-104 doi: 10.1016/S0165-0270(03)00167-5
[50] Zhang F, Li P F, Hou Z G, Lu Z, Chen Y X, Li Q L, Tan M. sEMG-based continuous estimation of joint angles of human legs by using BP neural networks. Neurocomputing, 2012, 78(1):139-148 doi: 10.1016/j.neucom.2011.05.033
[51] Jiang N, Vujaklija I, Rehbaum H, Graimann B, Farina D. Is accurate mapping of EMG signals on kinematics needed for precise online myoelectric control? IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2014, 22(3):549-558 doi: 10.1109/TNSRE.2013.2287383
[52] Artemiadis P K, Kyriakopoulos K J. EMG-based control of a robot arm using low-dimensional embeddings. IEEE Transactions on Robotics, 2010, 26(2):393-398 doi: 10.1109/TRO.2009.2039378
[53] Artemiadis P K, Kyriakopoulos K J. An EMG-based robot control scheme robust to time-varying EMG signal features. IEEE Transactions on Information Technology in Biomedicine, 2010, 14(3):582-588 doi: 10.1109/TITB.2010.2040832
[54] Yu H J, Lee A Y, Choi Y. Human elbow joint angle estimation using electromyogram signal processing. IET Signal Processing, 2011, 5(8):767-775 doi: 10.1049/iet-spr.2009.0203
[55] Ahmed M R. Compliance Control of Robot Manipulator for Safe Physical Human Robot Interaction[Ph.D. dissertation], Orebro University, Sweden, 2011.
[56] Tsuji T, Kaneko M. Estimation and modeling of human hand impedance during isometric muscle contraction. Proceedings of the ASME Dynamics Systems and Control Division, 1996, 58:575-582
[57] Shin D, Kim J, Koike Y. A myokinetic arm model for estimating joint torque and stiffness from EMG signals during maintained posture. Journal of Neurophysiology, 2009, 101(1):387-401 https://www.researchgate.net/publication/23469434_A_Myokinetic_Arm_Model_for_Estimating_Joint_Torque_and_Stiffness_From_EMG_Signals_During_Maintained_Posture
[58] Kim H K, Kang B, Kim B, Park S. Estimation of multijoint stiffness using electromyogram and artificial neural network. IEEE Transactions on Systems, Man, Cybernetics--Part A:Systems and Humans, 2009, 39(5):972-980 doi: 10.1109/TSMCA.2009.2025021
[59] Ajoudani A, Tsagarakis N, Bicchi A. Tele-impedance:teleoperation with impedance regulation using a body-machine interface. International Journal of Robotics Research, 2012, 31(13):1642-1656 doi: 10.1177/0278364912464668
[60] Liang P D, Yang C G, Wang N, Li Z J, Li R F, Burdet E. Implementation and test of human-operated and human-like adaptive impedance controls on Baxter robot. Advances in Autonomous Robotics Systems. Switzerland:Springer International Publishing, 2014. 109-119
[61] Pons J L, Rocon E, Ceres R, Reynaerts D, Saro B, Levin S, Van Moorleghem W. The MANUS-HAND dextrous robotics upper limb prosthesis:mechanical and manipulation aspects. Autonomous Robots, 2004, 16(2):143-163 doi: 10.1023/B:AURO.0000016862.38337.f1
[62] Pons J L, Ceres R, Rocon E, Levin S, Markovitz I, Saro B, Reynaerts D, Van Moorleghem W, Bueno L. Virtual reality training and EMG control of the MANUS hand prosthesis. Robotica, 2005, 23(3):311-317 doi: 10.1017/S026357470400133X
[63] Butterfass J, Grebenstein M, Liu H, Hirzinger G. DLR-Hand II:next generation of a dextrous robot hand. In:Proceedings of 2001 International Conference on Robotics and Automation. Seoul, Korea:IEEE, 2001. 109-114
[64] Butterfass J, Fischer M, Grebenstein M, Haidacher S, Hirzinger G. Design and experiences with the DLR hand II. In:Proceedings of the 2004 World Automation Congress. Seville, Spain:IEEE, 2004. 105-110
[65] 杨大鹏, 赵京东, 姜力, 刘宏.三自由度仿人型假手及其肌电控制策略.哈尔滨工程大学学报, 2009, 30(7):804-810 http://www.cnki.com.cn/Article/CJFDTOTAL-HEBG200907014.htm

Yang Da-Peng, Zhao Jing-Dong, Jiang Li, Liu Hong. A 3-DOF anthropomorphic prosthetic hand and its EMG control method. Journal of Harbin Engineering University, 2009, 30(7):804-810 http://www.cnki.com.cn/Article/CJFDTOTAL-HEBG200907014.htm
[66] He P, Jin M H, Yang L, Wei R, Liu Y W, Cai H G, Liu H, Seitz N, Butterfass J, Hirzinger G. High performance DSP/FPGA controller for implementation for HIT/DLR dexterous robot hand. In:Proceedings of 2004 International Conference on Robotics and Automation. New Orleans, USA:IEEE, 2004, 4:3397-3402
[67] Gaiser I N, Pylatiuk C, Schulz S, Kargov A, Oberle R, Werner T. The FLUIDHAND III:a multifunctional prosthetic hand. Journal of Prosthetics and Orthotics, 2009, 21(2):91-96 doi: 10.1097/JPO.0b013e3181a1ca54
[68] Antfolk C, Cipriani C, Controzzi M, Carrozza M C, Lundborg G, Rosen B. Using EMG for real-time prediction of joint angles to control a prosthetic hand equipped with a sensory feedback system. Journal of Medical and Biological Engineering, 2010, 30(6):399-406 doi: 10.5405/jmbe.767
[69] Otr O V D N, Reinders-Messelink H A, Bongers R M, Bouwsema H, Van Der Sluis C K. The i-LIMB hand and the DMC plus hand compared:a case report. Prosthetics and Orthotics International, 2010, 34(2):216-220 doi: 10.3109/03093641003767207
[70] Connolly C. Prosthetic hands from touch bionics. Industrial Robot:An International Journal, 2008, 35(4):290-293 doi: 10.1108/01439910810876364
[71] Perry J C. Design and development of a 7 degree-of-freedom powered exoskeleton for the upper limb[Ph.D. dissertation], University of Washington, USA, 2006.
[72] Hayashi T, Kawamoto H, Sankai Y. Control method of robot suit HAL working as operator's muscle using biological and dynamical information. In:Proceedings of the 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems. Alera, Canada:IEEE, 2005. 3063-3068
[73] Sankai Y. HAL:hybrid assistive limb based on cybernics. Robotics Research:Springer Tracts in Advanced Robotics. Berlin Heidelberg:Springer, 2011, 66:25-34
[74] Lee S, Sankai Y. Power assist control for walking aid with HAL-3 based on EMG and impedance adjustment around knee joint. In:Proceedings of the 2002 IEEE/RSJ International Conference on Intelligent Robots and Systems. Lausanne, Switzerland:IEEE, 2002, 2:1499-1504
[75] Kiguchi K, Hayashi Y. An EMG-based control for an upper-limb power-assist exoskeleton robot. IEEE Transactions on Systems, Man, Cybernetics--Part B:Cybernetics, 2012, 42(4):1064-1071 doi: 10.1109/TSMCB.2012.2185843
[76] Saponas T S, Tan D S, Morris D, Balakrishnan R, Turner J, Landay J A. Enabling always-available input with muscle-computer interfaces. In:Proceedings of the 22nd Annual Symposium on User Interface Software and Technology. New York, USA:ACM, 2009. 167-176
[77] Saponas T S, Tan D S, Morris D, Turner J, Landay J A. Making muscle-computer interfaces more practical. In:Proceedings of the 2010 SIGCHI Conference on Human Factors in Computing Systems. New York, USA:ACM, 2010. 851-854
[78] Han J D, Xiong A B, Zhao X G, Ding Q C, Chen Y G, Liu G J. sEMG based quantitative assessment of acupuncture on Bell's palsy:an experimental study. Science China Information Sciences, 2015, 58(8):1-15 http://www.cnki.com.cn/Article/CJFDTotal-JFXG201508009.htm
[79] Lee S W, Wilson K M, Lock B A, Kamper D G. Subject-specific myoelectric pattern classification of functional hand movements for stroke survivors. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2011, 19(5):558-566 doi: 10.1109/TNSRE.2010.2079334
[80] Kuiken T A, Li G L, Lock B A, Lipschutz R D, Miller L A, Stubblefield K A, Englehart K B. Targeted muscle reinnervation for real-time myoelectric control of multifunction artificial arms. JAMA, 2009, 301(6):619-628 doi: 10.1001/jama.2009.116
[81] Mesa I, Rubio A, Tubia I, De No J, Diaz J. Channel and feature selection for a surface electromyographic pattern recognition task. Expert Systems with Applications, 2014, 41(11):5190-5200 doi: 10.1016/j.eswa.2014.03.014
[82] 丁其川, 赵新刚, 韩建达.基于肌电信号容错分类的手部动作识别.机器人, 2015, 37(1):9-16 http://www.cnki.com.cn/Article/CJFDTOTAL-JQRR201501002.htm

Ding Qi-Chuan, Zhao Xin-Gang, Han Jian-Da. Recognizing hand motions based on fault-tolerant classification with EMG signals. Robot, 2015, 37(1):9-16 http://www.cnki.com.cn/Article/CJFDTOTAL-JQRR201501002.htm
[83] De Luca C J, Chang S S, Roy S H, Kline J C, Nawab S H. Decomposition of surface EMG signals from cyclic dynamic contractions. Journal of Neurophysiology, 2015, 113(6):1941-1951 doi: 10.1152/jn.00555.2014