State of the Art

  • Shane XieEmail author
  • Ye Ma
  • Wei Meng


A comprehensive literature review on biomechatronics input interfaces was carried out to identify the key issues in this field. The main design requirements and development complications were identified and the various approaches used in past interfaces were reviewed. The review begins with a survey of existing biological interfaces designed for use in human assistance and treatment. An overview of EEG and EMG based biomechanical models is also provided. This is followed by a review of the state-of-the-art in biomechanical model-based control strategies, with primary focus on its application to rehabilitation robots. Finally, the reviewed materials are discussed to highlight issues in biomechanics that require further work, and are hence the subject of investigation for this research.


  1. 1.
    Kübler, A., et al., Brain-computer communication: Unlocking the locked in. Psychological Bulletin, 2001. 127(3): p. 358–375.Google Scholar
  2. 2.
    Birbaumer, N., et al., A spelling device for the paralysed. Nature, 1999. 398(6725): p. 297–298.Google Scholar
  3. 3.
    Farwell, L.A. and E. Donchin, Talking off the top of your head - Toward a mental prosthesis utilizing event-related brain potentials. Electroencephalography and Clinical Neurophysiology, 1988. 70(6): p. 510–523.Google Scholar
  4. 4.
    Donchin, E., K.M. Spencer, and R. Wijesinghe, The mental prosthesis: assessing the speed of a P300-based brain-computer interface. IEEE Transactions on Rehabilitation Engineering, 2000. 8(2): p. 174–179.Google Scholar
  5. 5.
    Wolpaw, J.R. and D.J. McFarland, Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans. Proceedings of the National Academy of Sciences of the United States of America, 2004. 101(51): p. 17849–54.Google Scholar
  6. 6.
    McFarland, D.J., et al., Mu and beta rhythm topographies during motor imagery and actual movements. Brain Topography, 2000. 12(3): p. 177–186.Google Scholar
  7. 7.
    Kostov, A. and M. Polak, Parallel man-machine training in development of EEG-based cursor control. IEEE Transactions in Rehabilitation Engineering, 2000. 8(2): p. 203–205.Google Scholar
  8. 8.
    Wolpaw, J.R. and D.J. Mcfarland, Multichannel EEG-Based brain-computer communication. Electroencephalography and Clinical Neurophysiology, 1994. 90(6): p. 444–449.Google Scholar
  9. 9.
    Wolpaw, J.R., et al., The Wadsworth Center brain-computer interface (BCI) research and development program. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2003. 11(2): p. 204–207.Google Scholar
  10. 10.
    Niedermeyer, E. and F.L.d. Silva, Electroencephalography Basic Principles, Clinical Applications, and Related Fields. 5th ed. 2004: Lippincott Williams & Wilkins. 1256 pages.Google Scholar
  11. 11.
    Wolpaw, J. and E.W. Wolpaw, Brain-Computer Interfaces: Principles and Practice. 1 ed. 2012: Oxford University Press.Google Scholar
  12. 12.
    Guangyu, B., et al., VEP-based brain-computer interfaces: Time, frequency, and code modulations. IEEE Computational Intelligence Magazine, 2009. 4(4): p. 22–26.Google Scholar
  13. 13.
    Sutter, E.E., The brain response interface: Communication through visually-induced electrical brain responses. Journal of Microcomputer Applications, 1992. 15(1): p. 31–45.Google Scholar
  14. 14.
    Trejo, L.J., R. Rosipal, and B. Matthews, Brain-computer interfaces for 1-D and 2-D cursor control: Designs using volitional control of the EEG spectrum or steady-state visual evoked potentials. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2006. 14(2): p. 225–229.Google Scholar
  15. 15.
    Martinez, P., H. Bakardjian, and A. Cichocki, Fully online multicommand brain-computer interface with visual neurofeedback using SSVEP paradigm. Computational Intelligence and Neuroscience & Biobehavioral Reviews, 2007. 2007: p. 9.Google Scholar
  16. 16.
    Faller, J., et al., Avatar navigation in virtual and augmented reality environments using an SSVEP BCI. in International Conference on Applied Bionics and Biomechanics 2010: Venice, Italy. p. 1–4.Google Scholar
  17. 17.
    McDaid, A.J., S. Xing, and S.Q. Xie. Brain controlled robotic exoskeleton for neurorehabilitation. in 2013 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, 2013. Wollongong, Australia.Google Scholar
  18. 18.
    Song, X., S.Q. Xie, and K.C. Aw, EEG-based brain computer interface for game control, in International Conference on Affective Computing and Intelligent Interaction. 2012: Taipei, Taiwan. p. 47–54.Google Scholar
  19. 19.
    Kelly, S.P., et al., Visual spatial attention tracking using high-density SSVEP data for independent brain-computer communication. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2005. 13(2): p. 172–178.Google Scholar
  20. 20.
    Allison, B.Z., et al., Towards an independent brain-computer interface using steady state visual evoked potentials. Clinical Neurophysiology, 2008. 119(2): p. 399–408.Google Scholar
  21. 21.
    Goncharova, I.I., et al., EMG contamination of EEG: Spectral and topographical characteristics. Clinical Neurophysiology, 2003. 114(9): p. 1580–1593.Google Scholar
  22. 22.
    Fatourechi, M., et al., EMG and EOG artifacts in brain computer interface systems: A survey. Clinical Neurophysiology, 2007. 118(3): p. 480–494.Google Scholar
  23. 23.
    Anderer, P., et al., Artifact processing in computerized analysis of sleep EEG – A review. Neuropsychobiology, 1999. 40(3): p. 150–157.Google Scholar
  24. 24.
    Jung, T.-P., et al., Removal of eye activity artifacts from visual event-related potentials in normal and clinical subjects. Clinical Neurophysiology, 2000. 111(10): p. 1745–1758.Google Scholar
  25. 25.
    McFarland, D.J., et al., Spatial filter selection for EEG-based communication. Electroencephalography and Clinical Neurophysiology, 1997. 103(3): p. 386–394.Google Scholar
  26. 26.
    Gupta, S. and H. Singh. Preprocessing EEG signals for direct human-system interface. in IEEE International Joint Symposia on Intelligence and Systems, 1996.Google Scholar
  27. 27.
    Bostanov, V., BCI competition 2003-Data sets Ib and IIb: Feature extraction from event-related brain potentials with the continuous wavelet transform and the t-value scalogram. IEEE Transactions on Biomedical Engineering, 2004. 51(6): p. 1057–1061.Google Scholar
  28. 28.
    Lei, Q. and H. Bin, A wavelet-based time–frequency analysis approach for classification of motor imagery for brain–computer interface applications. Journal of Neural Engineering, 2005. 2(4): p. 65.Google Scholar
  29. 29.
    Garcia, G.N., T. Ebrahimi, and J.M. Vesin. Correlative exploration of EEG signals for direct brain-computer communication. in IEEE International Conference on Acoustics, Speech, and Signal, 2003.Google Scholar
  30. 30.
    Flotzinger, D., M. Pregenzer, and G. Pfurtscheller. Feature selection with distinction sensitive learning vector quantisation and genetic algorithms. in IEEE World Congress on Computational Intelligence, 1994.Google Scholar
  31. 31.
    Pregenzer, M. and G. Pfurtscheller, Frequency component selection for an EEG-based brain to computer interface. IEEE Transactions on Rehabilitation Engineering, 1999. 7(4): p. 413–419.Google Scholar
  32. 32.
    Chaiyaratana, N. and A.M.S. Zalzala. Recent developments in evolutionary and genetic algorithms: Theory and applications. in Genetic Algorithms in Engineering Systems: Innovations and Applications, 1997.Google Scholar
  33. 33.
    Müller, K.R., C.W. Anderson, and G.E. Birch, Linear and nonlinear methods for brain-computer interfaces. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2003. 11(2): p. 165–169.Google Scholar
  34. 34.
    Bayliss, J.D., S.A. Inverso, and A. Tentler, Changing the P300 brain computer interface. Cyberpsychology & Behavior, 2004. 7(6): p. 694–704.Google Scholar
  35. 35.
    Anderson, C.W., E.A. Stolz, and S. Shamsunder, Multivariate autoregressive models for classification of spontaneous electroencephalographic signals during mental tasks. IEEE Transactions on Biomedical Engineering, 1998. 45(3): p. 277–286.Google Scholar
  36. 36.
    Palaniappan, R. Brain computer interface design using band powers extracted during mental tasks. in IEEE EMBS Conference on Neural Engineering, 2005.Google Scholar
  37. 37.
    Peterson, D.A., et al., Feature selection and blind source separation in an EEG-based brain-computer interface. Journal on Applied Signal Processing, 2005. 2005(19): p. 3128–3140.Google Scholar
  38. 38.
    Haselsteiner, E. and G. Pfurtscheller, Using time-dependent neural networks for EEG classification. IEEE Transactions on Rehabilitation Engineering, 2000. 8(4): p. 457–63.Google Scholar
  39. 39.
    Ferrez, P.W. and J. del R. Millan, Error-related EEG potentials generated during simulated brain-computer interaction. IEEE Transactions on Biomedical Engineering, 2008. 55(3): p. 923–929.Google Scholar
  40. 40.
    Wolpaw, J.R., et al., EEG-based communication: Improved accuracy by response verification. IEEE Transactions on Rehabilitation Engineering, 1998. 6(3): p. 326–333.Google Scholar
  41. 41.
    Millan, J.d., et al. Neural networks for robust classification of mental tasks. in Proceedings of the 22nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2000.Google Scholar
  42. 42.
    Millan, J.R., et al., A local neural classifier for the recognition of EEG patterns associated to mental tasks. IEEE Transactions on Neural Networks, 2002. 13(3): p. 678–686.Google Scholar
  43. 43.
    Peters, B.O., G. Pfurtscheller, and H. Flyvbjerg, Automatic differentiation of multichannel EEG signals. IEEE Transactions on Biomedical Engineering, 2001. 48(1): p. 111–116.Google Scholar
  44. 44.
    McFarland, D.J., L.M. McCane, and J.R. Wolpaw, EEG-based communication and control: Short-term role of feedback. IEEE Transactions on Rehabilitation Engineering, 1998. 6(1): p. 7–11.Google Scholar
  45. 45.
    Siniatchkin, M., P. Kropp, and W.-D. Gerber, Neurofeedback—The significance of reinforcement and the search for an appropriate strategy for the success of self-regulation. Applied Psychophysiology and Biofeedback, 2000. 25(3): p. 167–175.Google Scholar
  46. 46.
    Carter, C.S., et al., The role of the anterior cingulate cortex in error detection and the on-line monitoring of performance: An event related fMRI study. Biological Psychiatry, 1998. 43: p. 13s.Google Scholar
  47. 47.
    Carter, C.S., et al., Anterior cingulate cortex, error detection, and the online monitoring of performance. Science, 1998. 280(5364): p. 747–749.Google Scholar
  48. 48.
    Holroyd, C.B. and M.G.H. Coles, The neural basis of human error processing: Reinforcement learning, dopamine and the error-related negativity. Psychological Review, 2002. 109(4): p. 679–709.Google Scholar
  49. 49.
    van Schie, H.T., et al., Modulation of activity in medial frontal and motor cortices during error observation. Nature Neuroscience, 2004. 7(5): p. 549–554.Google Scholar
  50. 50.
    Schalk, G., et al., EEG-based communication: Presence of an error potential. Clinical Neurophysiology, 2000. 111(12): p. 2138–2144.Google Scholar
  51. 51.
    Mason, S.G. and G.E. Birch, A general framework for brain-computer interface design. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2003. 11(1): p. 70–85.Google Scholar
  52. 52.
    Cavanagh P.R., and P.V. Komi, Electromechanical delay in human skeletal muscle under concentric and eccentric contractions. European Journal of Applied Physiology and Occupational Physiology, 1979. 42(3): p. 159–163.Google Scholar
  53. 53.
    Cheung K.M., et al., Recent advances in the aetiology of adolescent idiopathic scoliosis. International Orthopaedics, 2008. 32(6): p. 729–734.Google Scholar
  54. 54.
    Wu W., et al., Application of surface EMG in evaluation of effectiveness of clinical interventions for lumbar intervertebral disc prolapse. Chinese Journal of Physical Medicine and Rehabilitation, 2002. 9.Google Scholar
  55. 55.
    Neblett R., et al., A clinical guide to surface-EMG-assisted stretching as an adjunct to chronic musculoskeletal pain rehabilitation. Applied Psychophysiology and Biofeedback, 2003. 28(2): p. 147–160.Google Scholar
  56. 56.
    Kralj A.R., and T. Bajd, Functional electrical stimulation: Standing and walking after spinal cord injury: CRC press, 1989.Google Scholar
  57. 57.
    Jezernik S., et al., Robotic orthosis lokomat: A rehabilitation and research tool. Neuromodulation, 2003. 6(2): p. 108–115.Google Scholar
  58. 58.
    Sankai Y., HAL: Hybrid assistive limb based on cybernics. Robotics Research, 2010. p. 25–34.Google Scholar
  59. 59.
    Huston L.J., and E.M. Wojtys, Neuromuscular performance characteristics in elite female athletes. The American Journal of Sports Medicine, 1996. 24(4): p. 427–436, 1996.Google Scholar
  60. 60.
    Lovely R.C.-D., Commercial Hardware for the Implementation of Myoelectric Control. Powered Upper Limb Prostheses: Control, Implementation and Clinical Application, 2004.Google Scholar
  61. 61.
    Huang Z.-X., X.-D. Zhang, and Y.-N. Li, Design of a grasp force adaptive control system with tactile and slip perception. in IEEE International Conference on Automation Science and Engineering, August 20–24, 2012. p. 1101–1105.Google Scholar
  62. 62.
    Jong-Sung K., Huyk J., and Wookho S., A new means of HCI: EMG-MOUSE. in IEEE International Conference on Systems, Man and Cybernetics, October 10–13, 2004. p. 100–104.Google Scholar
  63. 63.
    Costanza E., S.A. Inverso, and R. Allen, Toward subtle intimate interfaces for mobile devices using an EMG controller. in Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 2005. p. 481–489.Google Scholar
  64. 64.
    Aso S., et al., Driving electric car by using EMG interface. IEEE Conference on Cybernetics and Intelligent Systems, June 7–9, 2006. p. 1–5.Google Scholar
  65. 65.
    Wheeler K.R., and C.C. Jorgensen, Gestures as input: Neuroelectric joysticks and keyboards. IEEE Pervasive Computing, 2003. 2(2): p. 56–61.Google Scholar
  66. 66.
    Wheeler K.R., Device control using gestures sensed from EMG. in IEEE International Workshop on Soft Computing in Industrial Applications, June 22–25, 2003. p. 21–26.Google Scholar
  67. 67.
    Feng C.J., A.F. Mak, and T.K. Koo, A surface EMG driven musculoskeletal model of the elbow flexion-extension movement in normal subjects and in subjects with spasticity. Journal of Musculoskeletal Research, 1999. 3(2): p. 109–123.Google Scholar
  68. 68.
    Buchanan T., S. Delp, and J. Solbeck, Muscular resistance to varus and valgus loads at the elbow. Journal of Biomechanical Engineering, 1998. 120(5): p. 634.Google Scholar
  69. 69.
    Soechting J., and M. Flanders, Evaluating an integrated musculoskeletal model of the human arm. Journal of Biomechanical Engineering, 1997. 119(1): p. 93.Google Scholar
  70. 70.
    Laursen B., B.R. Jensen, G. Németh, and G. Sjøgaard, A model predicting individual shoulder muscle forces based on relationship between electromyographic and 3D external forces in static position. Journal of Biomechanics, 1998. 31(8): p. 731.Google Scholar
  71. 71.
    Lloyd D.G., and T.F. Besier, An EMG-driven musculoskeletal model to estimate muscle forces and knee joint moments in vivo. Journal of Biomechanics, 2003. 36(6): p. 765–776.Google Scholar
  72. 72.
    Ferris D.P., et al., An improved powered ankle–foot orthosis using proportional myoelectric control. Gait & Posture, 2006. 23(4): p. 425–428.Google Scholar
  73. 73.
    Granata K.P., and W. Marras, An EMG-assisted model of trunk loading during free-dynamic lifting. Journal of Biomechanics, 1995. 28(11): p. 1309–1317.Google Scholar
  74. 74.
    Nussbaum M.A., and D.B. Chaffin, Lumbar muscle force estimation using a subject-invariant 5-parameter EMG-based model. Journal of Biomechanics, 1998. 31(7): p. 667–672.Google Scholar
  75. 75.
    Buchanan T.S., et al., Estimation of muscle forces about the wrist joint during isometric tasks using an EMG coefficient method. Journal of Biomechanics, 1993. 26(4–5): p. 547–560.Google Scholar
  76. 76.
    Buchanan T.S., et al., Neuromusculoskeletal modeling: Estimation of muscle forces and joint moments and movements from measurements of neural command. Journal of Applied Biomechanics, 2004. 20(4): p. 367–395.Google Scholar
  77. 77.
    Knaepen, K., et al., Human-robot interaction: Kinematics and muscle activity inside a powered compliant knee exoskeleton. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2014.Google Scholar
  78. 78.
    Cavallaro F., Fuzzy TOPSIS approach for assessing thermal-energy storage in concentrated solar power (CSP) systems, Applied Energy, vol. 87, no. 2, p. 496–503, 2010.Google Scholar
  79. 79.
    Shao Q., et al., An EMG-driven model to estimate muscle forces and joint moments in stroke patients. Computers in Biology and Medicine, 2009. 39(12): p. 1083–1088.Google Scholar
  80. 80.
    Sartori M., et al., Fast operation of anatomical and stiff tendon neuromuscular models in EMG-driven modeling. in IEEE International Conference on Robotics and Automation, 2010. p. 2228–2234.Google Scholar
  81. 81.
    Au A.T., and R.F. Kirsch, EMG-based prediction of shoulder and elbow kinematics in able-bodied and spinal cord injured individuals. IEEE Transactions on Rehabilitation Engineering, 2000. 8(4): p. 471–480, 2000.Google Scholar
  82. 82.
    Artemiadis P.K., and K.J. Kyriakopoulos, EMG-based control of a robot arm using low-dimensional embeddings. IEEE Transactions on Robotics, 2010. 26(2): p. 393–398.Google Scholar
  83. 83.
    Pau J.W., S.Q. Xie, and A.J. Pullan, Neuromuscular interfacing: Establishing an EMG-driven model for the human elbow joint. IEEE Transactions on Biomedical Engineering, 2012. 59(9): p. 2586–2593.Google Scholar
  84. 84.
    Pau J.W.L., et al., An EMG-driven neuromuscular interface for human elbow joint. 3rd IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics, September 26–29, 2010. p. 156-161.Google Scholar
  85. 85.
    Delp, S.L., et al., An interactive graphics-based model of the lower extremity to study orthopaedic surgical procedures. IEEE Transactions on Biomedical Engineering, 1990. 37(8): p. 757–767.Google Scholar
  86. 86.
    Koo, T.K.K. and A.F.T. Mak, Feasibility of using EMG driven neuromusculoskeletal model for prediction of dynamic movement of the elbow. Journal of Electromyography and Kinesiology, 2005. 15(1): p. 12–26.Google Scholar
  87. 87.
    Damsgaard, M., et al., Analysis of musculoskeletal systems in the AnyBody Modeling System. Simulation Modelling Practice and Theory, 2006. 14(8): p. 1100–1111.Google Scholar
  88. 88.
    Finni, T., P.V. Komi, and J. Lukkariniemi, Achilles tendon loading during walking: Application of a novel optic fiber technique. European Journal of Applied Physiology and Occupational Physiology, 1998. 77(3): p. 289–291.Google Scholar
  89. 89.
    Komi, P.V., S. Fukashiro, and M. Järvinen, Biomechanical loading of Achilles tendon during normal locomotion. Clinics in Sports Medicine, 1992. 11(3): p. 521–531.Google Scholar
  90. 90.
    Dennerlein, J.T., et al., Tensions of the flexor digitorum superficialis are higher than a current model predicts. Journal of biomechanics, 1998. 31(4): p. 295–301.Google Scholar
  91. 91.
    Seireg, A. and R.J. Arvikar, The prediction of muscular load sharing and joint forces in the lower extremities during walking. Journal of Biomechanics, 1975. 8(2): p. 89–102.Google Scholar
  92. 92.
    Crowninshield, R.D. and R.A. Brand, A physiologically based criterion of muscle force prediction in locomotion. Journal of Biomechanics, 1981. 14(11): p. 793–801.Google Scholar
  93. 93.
    Crowninshield, R.D., et al., A biomechanical investigation of the human hip. Journal of Biomechanics, 1978. 11(1–2): p. 75–85.Google Scholar
  94. 94.
    Yamaguchi, G.T. and F.E. Zajac, Restoring unassisted natural gait to paraplegics via functional neuromuscular stimulation: A computer simulation study. IEEE Transactions on Biomedical Engineering, 1990. 37(9): p. 886–902.Google Scholar
  95. 95.
    Röhrle, H., et al., Joint forces in the human pelvis-leg skeleton during walking. Journal of Biomechanics, 1984. 17(6): p. 409–424.Google Scholar
  96. 96.
    Brand, R.A., D.R. Pedersen, and J.A. Friederich, The sensitivity of muscle force predictions to changes in physiologic cross-sectional area. Journal of Biomechanics, 1986. 19(8): p. 589–596.Google Scholar
  97. 97.
    Collins, J.J., The redundant nature of locomotor optimization laws. Journal of Biomechanics, 1995. 28(3): p. 251–267.Google Scholar
  98. 98.
    Dul, J., et al., Muscular synergism—I. On criteria for load sharing between synergistic muscles. Journal of Biomechanics, 1984. 17(9): p. 663–673.Google Scholar
  99. 99.
    Li, G., et al., Prediction of antagonistic muscle forces using inverse dynamic optimization during flexion/extension of the knee. Journal of Biomechanical Engineering, 1999. 121(3): p. 316–322.Google Scholar
  100. 100.
    Forster, E., et al., Extension of a state-of-the-art optimization criterion to predict co-contraction. Journal of Biomechanics, 2004. 37(4): p. 577–581.Google Scholar
  101. 101.
    Knutsson, E. and C. Richards, Different types of disturbed motor control in gait of hemiparetic patients. Brain, 1979. 102(2): p. 405–430.Google Scholar
  102. 102.
    Perry J., et al., Gait analysis of the triceps surae in cerebral palsy: A preoperative and postoperative clinical and electromyographic study. Journal of Bone & Joint Surgery, 1974. 56(3): p. 511–520.Google Scholar
  103. 103.
    Zajac, F.E., Muscle and tendon: Properties, models, scaling, and application to biomechanics and motor control. Critical Reviews in Biomedical Engineering, 1989. 17(4): p. 359–411.Google Scholar
  104. 104.
    Carozza, M. C., et al. “On the development of a novel adaptive prosthetic hand with compliant joints: experimental platform and EMG control.” 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE, 2005.Google Scholar
  105. 105.
    Yang, Da-peng, et al. “An anthropomorphic robot hand developed based on underactuated mechanism and controlled by EMG signals.” Journal of Bionic Engineering, 2009, 6(3): p. 255–263.Google Scholar
  106. 106.
    Manal, K., et al., A real-time EMG-driven virtual arm. Computers in Biology and Medicine, 2002. 32(1): p. 25–36.Google Scholar
  107. 107.
    Bogey, R.A., J. Perry, and A.J. Gitter, An EMG-to-force processing approach for determining ankle muscle forces during normal human gait. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2005. 13(3): p. 302–310.Google Scholar
  108. 108.
    Gordon, A., A.F. Huxley, and F. Julian, The variation in isometric tension with sarcomere length in vertebrate muscle fibres. The Journal of physiology, 1966. 184(1): p. 170–192.Google Scholar
  109. 109.
    Buchanan, T.S., et al., Estimation of muscle forces and joint moments using a forward-inverse dynamics model. Medicine and Science in Sports and exercise, 2005. 37(11): p. 1911.Google Scholar
  110. 110.
    Tong, R. Biomechatronics in medicine and healthcare. Pan Stanford Publishing, 2011.Google Scholar
  111. 111.
    Sartori, M., et al. An EMG-driven musculoskeletal model of the human lower limb for the estimation of muscle forces and moments at the hip, knee and ankle joints in vivo. in Proceedings of International Conference on Simulation, Modeling and Programming for Autonomous Robots, 2010.Google Scholar
  112. 112.
    Sartori, M., et al., 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): p. e52618.Google Scholar
  113. 113.
    Thelen, D.G., Adjustment of muscle mechanics model parameters to simulate dynamic contractions in older adults. Journal of Biomechanical Engineering, 2003. 125(1): p. 70–77.Google Scholar
  114. 114.
    Pau, J.W.L., et al., An EMG-driven neuromuscular interface for human elbow joint. in IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics, 2010.Google Scholar
  115. 115.
    Carrozza MC, A wearable biomechatronic interface for controlling robots with voluntary foot movements. IEEE/ASME Transactions on Mechatronics. 2007, 12(1): p. 1–1.Google Scholar
  116. 116.
    Ding, Q.C., et al., A novel EMG-driven state space model for the estimation of continuous joint movements. in IEEE International Conference on Systems, Man, and Cybernetics, 2011.Google Scholar
  117. 117.
    Song, Q., et al., A real-time EMG-driven arm wrestling robot considering motion characteristics of human upper limbs. International Journal of Humanoid Robotics, 2007. 4(4): p. 645–670.Google Scholar
  118. 118.
    Ryu, W., B. Han, and J. Kim. Continuous position control of 1 DOF manipulator using EMG signals. in Third International Conference on Convergence and Hybrid Information Technology, 2008.Google Scholar
  119. 119.
    Jain R.K., Design and control of an IPMC artificial muscle finger for micro gripper using EMG signal. Mechatronics, 2013, 23(3): p. 381–94.Google Scholar
  120. 120.
    Al-Jumaily, A. and R.A. Olivares, Bio-driven system-based virtual reality for prosthetic and rehabilitation systems. Signal, Image and Video Processing, 2012. 6(1): p. 71–84.Google Scholar
  121. 121.
    Sartori, M., G. Chemello, and E. Pagello, A 3D virtual model of the knee driven by EMG signals. in Artificial Intelligence and Human-Oriented Computing, 2007. p. 591–601.Google Scholar
  122. 122.
    Wolpaw, J.R., et al., Brain-computer interfaces for communication and control. Clinical Neurophysiology, 2002. 113(6): p. 767–791.Google Scholar
  123. 123.
    Allison, B. and J. Jacko, The I of BCIs: Next Generation Interfaces for Brain–Computer Interface Systems That Adapt to Individual Users, in Human-Computer Interaction. Novel Interaction Methods and Techniques. 2009, Springer Berlin/ Heidelberg. p. 558–568.Google Scholar
  124. 124.
    Petrofsky J.S., and C.A. Phillips, Interactions between fatigue, muscle temperature, blood flow and the surface EMG. NAECON, 1980. p. 520–527.Google Scholar
  125. 125.
    Moritani T., M. Muro, and A. Nagata, Intramuscular and surface electromyogram changes during muscle fatigue. Journal of Applied Physiology, 1986. 60(4): p. 1179–1185.Google Scholar
  126. 126.
    Park E., and S.G. Meek, Fatigue compensation of the electromyographic signal for prosthetic control and force estimation. IEEE Transactions on Biomedical Engineering, 1993. 40(10): p. 1019–1023.Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.School of Electrical and Electronic EngineeringUniversity of LeedsLeedsUnited Kingdom
  2. 2.Department of Mechanical EngineeringThe University of AucklandAucklandNew Zealand
  3. 3.School of Information EngineeringWuhan University of TechnologyWuhanChina
  4. 4.Ningbo UniversityNingboChina

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