Lina Tong, Feng Zhang, Zeng-Guang Hou, Weiqun Wang, and Liang Peng


  1. [1] R.L. Chen, J.S. Balami, M.M. Esiri, L.K. Chen, and A.M. Buchan, Ischemic stroke in the elderly: An overview of evidence, Nature Reviews Neurology, 6, 2010, 256–265.
  2. [2] G.A. Donnan, M. Fisher, M. Macleod, and S.M. Davis, Stroke, The Lancet, 371(9624), 2008, 1612–1623.
  3. [3] E.C. Coffey, J.L. Cummings, S. Starkstein, and R. Robinson, Stroke – The American psychiatric press textbook of geriatric neuropsychiatry, 2nd ed. (Washington, DC: American Psychiatric Press, 2000).
  4. [4] J. Liepert, H. Bauder, H.R. Wolfgang, W.H. Miltner, E. Taub, and C. Weiller, Treatment-induced cortical recognization after stroke in humans, Stroke, 31, 2000, 1210–1216.
  5. [5] M. Lotze, C. Braun, N. Birbaumer, S. Anders, and L.G. Cohen, Motor learning elicited by voluntary drive, Brain, 126, 2003, 866–872.
  6. [6] G. Rosati, P. Gallina, and S. Masiero, Design, implementation and clinical tests of a wire-based robot for neurorehabilitation, IEEE Transactions on Neural Systems and Rehabilitation Engineering, 15(4), 2007, 560–569.
  7. [7] L. Weiss, J.K. Silver, and J. Weiss, EASY EMG: A guide to performing nerve conduction studies and electromyography (Singapore, Butterworth Heinemann Publisher, 2004).
  8. [8] G. Kamen and D. Gabriel, Essentials of electromyography (Champaign, Illinois, USA: Human Kinetics Publishers, 2009).
  9. [9] C. Frigo and P. Crenna, Multichannel SEMG in clinical gait analysis: A review and state-of-the-art, Clinical Biomechanics, 24, 2009, 236–245.
  10. [10] F. Yuanjie, G. Zhao, and Y. Yuehong, SEMG-based neuro-fuzzy controller for a parallel ankle exoskeleton with proprioception, International Journal of Robotics and Automation, 26(4), 2011, 450–455.
  11. [11] G.R. Naik, D.K. Kumar, and Jayadeva, Twin SVM for gesture classification using the surface electromyogram, IEEE Transactions on Information Technology in Biomedicine, 14(2), 2010, 301–308.
  12. [12] K. Momen, S. Krishnan, and T. Chau, Electromyographic signals corresponding to user-selected intentional movements for multifunction prosthesis control, IEEE Transactions on Neural Systems and Rehabilitation Engineering, 15(4), 2007, 535–542.
  13. [13] C.A.M. Doorenboscha and J. Harlaar, Accuracy of a practicable EMG to force model for knee muscles, Neuroscience Letters, 368, 2004, 78–81.
  14. [14] C. Choi, S. Kwon, W. Park, H. Lee, and Jung Kim, Real-time pinch force estimation by surface electromyography using an artificial neural network, Medical Engineering & Physics, 32, 2010, 429–436.
  15. [15] D. Staudenmann, K. Roeleveld, D.F. Stegeman, and J.H. van Dieën, Methodological aspects of sEMG recordings for force estimation – A tutorial and review, Journal of Electromyography and Kinesiology, 20, 2010, 375–387.
  16. [16] H. Huang, D.P. Yang, C.Y. Sun, N. Li, Y.J. Pang, L. Jiang, and H. Liu, Surface EMG for multi-pattern recognition with sensory feedback controller of hand prosthesis system, International Journal of Robotics and Automation, 28, 2013, 21–30.
  17. [17] N.A. Shrirao, N.P. Reddy, and D.R. Kosuri, Neural network committees for finger joint angle estimation from surface EMG signals, BioMedical Engineering OnLine, 8(2), 2009, 1–11.
  18. [18] F. Zhang, P. Li, Z.G. Hou, Z. Lu, Y. Chen, Q. Li, and M. Tan, sEMG-based continuous estimation of joint angles of human legs by using BP neural network, Neurocomputing, 78, 2012, 139–148.
  19. [19] C. Werner, A. Bardeleben, K.H. Mauritz, S. Kirker, and S. Hesse, Treadmill training with partial body weight support and physiotherapy in stroke patients: A preliminary comparison, European Journal of Neurology, 9, 2002, 639–644.
  20. [20] H.J. Eich, H. Mach, C. Werner, and S. Hesse, Aerobic treadmill plus Bobath walking training improves walking in subacute stroke: A randomized controlled trial, Clinical Rehabilitation, 18(6), 2004, 640–651.
  21. [21] H.S. Ryait, A.S. Arora, and R. Agarwal, Interpretations of wrist/grip operations from sEMG signals at different locations on arm, IEEE Transactions on Biomedical Circuits and Systems, 4(2), 2010, 101–111.
  22. [22] J. Wang, Some advances in the research of sEMG signal analysis and its application, Sports Science, 20(4), 2000, 56–60.
  23. [23] R. Bos, S. De Waele, and P.M.T. Broersen, Autoregressive spectral estimation by application of the Burg algorithm to irregularly sampled data, IEEE Transactions on Instrumentation and Measurement, 51(6), 2002, 1289–1294.
  24. [24] Shi-Lian Gao, Lower limb atlas of practical anatomy, 3rd ed. (Shanghai: Shanghai Scientific and Technical Publishers, 2012).
  25. [25] P. Konrad, The ABC of EMG: A practical introduction to kinesiological electromyography, 2013, www.noraxon.com.
  26. [26] R.N. Khushaba, S. Kodagoda, M. Takruri, and G. Dissanayake, Toward improved control of prosthetic fingers using surface electromyogram (EMG) signals, Expert Systems with Applications, 39, 2012, 10731–10738.
  27. [27] E.A. Clancy, L. Liu, P. Liu, and D.V.Z. Moyer, Identification of constant-posture EMG – Torque relationship about the elbow using nonlinear dynamic models, IEEE Transactions on Biomedical Engineering, 59(1), 2012, 205–212.
  28. [28] M.H. Hassoun, Fundamentals of artificial neural networks (UK: Bradford Books, 1995).
  29. [29] P.J. Brockwell, R. Dahlhaus, and A.A. Trindade, Modified Burg algorithms for multivariate subset autoregression, Statistica Sinica, 15, 2005, 197–213.
  30. [30] P.K. Artemiadis, and K.J. Kyriakopoulos, An EMG-based robot control scheme robust to time-varying EMG signal features, IEEE Transactions on Information Technology in Biomedicine, 14(3), 2010, 582–588.

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