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Conclusions and Future Prospects

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Biomechatronics in Medical Rehabilitation

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Abstract

Various technologies in developing biomechatronic systems for medical rehabilitation have been discussed in previous chapters. These included bio-signals processing, biomechanics modelling, neural and muscular interfaces, robot-assisted training, clinical implementation, and rehabilitation robot control. This chapter summarises the main outcomes and conclusions of this book, as well as highlight the contributions made by the authors. This chapter also provides a discussion of future directions that can be explored to extend or advance the work presented in this book.

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References

  1. Pau J.W.L., 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 

  2. 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 

  3. Eilenberg M.F., H. Geyer, and H. Herr, Control of a powered ankle–foot prosthetic based on a neuromuscular model. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2010. 18(2): p. 164–173.

    Google Scholar 

  4. Cavallaro E., et al., Hill-based model as a myoprocessor for a neural controlled powered exoskeleton arm-parameters optimization. IEEE International Conference on Robotics and Automation, 2005. p. 4514–4519.

    Google Scholar 

  5. Buchanan T.S., S.L. Delp, and J. Solbeck, Muscular resistance to varus and valgus loads at the elbow. Journal of Biomechanical Engineering, 1998. 120(5): p. 634–639.

    Google Scholar 

  6. Chang Y.-W., et al., Optimum length of muscle contraction. Clinical Biomechanics, 1999. 14(8): p. 537–542.

    Google Scholar 

  7. Koo T.K., A.F. Mak, and L. Hung, In vivo determination of subject-specific musculotendon parameters: Applications to the prime elbow flexors in normal and hemiparetic subjects. Clinical Biomechanics, 2002. 17(5): p. 390–399.

    Google Scholar 

  8. 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 

  9. Koenig, A., et al., Psychological state estimation from physiological recordings during robot-assisted gait rehabilitation. Journal of Rehabilitation Research & Development, 2011. 48(4): p. 367–385.

    Google Scholar 

  10. Glitsch, U. and W. Baumann, The three-dimensional determination of internal loads in the lower extremity. Journal of Biomechanics, 1997. 30(11–12): p. 1123–1131.

    Google Scholar 

  11. 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 

  12. 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 

  13. Patriarco, A.G., et al., An evaluation of the approaches of optimization models in the prediction of muscle forces during human gait. Journal of Biomechanics, 1981. 14(8): p. 513–525.

    Google Scholar 

  14. Anderson, F.C. and M.G. Pandy, Static and dynamic optimization solutions for gait are practically equivalent. Journal of Biomechanics, 2001. 34(2): p. 153–161.

    Google Scholar 

  15. Delp, S.L., et al., OpenSim: Open-source software to create and analyze dynamic simulations of movement. IEEE Transactions on Biomedical Engineering, 2007. 54(11): p. 1940–1950.

    Google Scholar 

  16. Riener, R., et al., Patient-cooperative strategies for robot-aided treadmill training: First experimental results. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2005. 13(3): p. 380–394.

    Google Scholar 

  17. Burgar, C.G., et al., Development of robots for rehabilitation therapy: The Palo Alto VA/Stanford experience. Journal of Rehabilitation Research and Development, 2000. 37(6): p. 663–674.

    Google Scholar 

  18. Husemann, B., et al., Effects of locomotion training with assistance of a robot-driven gait orthosis in hemiparetic patients after stroke a randomized controlled pilot study. Stroke, 2007. 38(2): p. 349–354.

    Google Scholar 

  19. Cai, L.L., et al., Implications of assist-as-needed robotic step training after a complete spinal cord injury on intrinsic strategies of motor learning. The Journal of Neuroscience, 2006. 26(41): p. 10564–10568.

    Google Scholar 

  20. Jezernik, S., et al., Adaptive robotic rehabilitation of locomotion: A clinical study in spinally injured individuals. Spinal Cord, 2003. 41(12): p. 657–666.

    Google Scholar 

  21. Beyl, P., et al., Safe and compliant guidance by a powered knee exoskeleton for robot-assisted rehabilitation of gait. Advanced Robotics, 2011. 25(5): p. 513–535.

    Google Scholar 

  22. Kong, K., et al. Mechanical design and impedance compensation of SUBAR (Sogang University’s Biomedical Assist Robot). in IEEE/ASME International Conference on Advanced Intelligent Mechatronics, August 2–5, 2008. Xi’an, China: Institute of Electrical and Electronics Engineers Inc.

    Google Scholar 

  23. Gupta, A., et al., Design, control and performance of rice wrist: A force feedback wrist exoskeleton for rehabilitation and training. International Journal of Robotics Research, 2008. 27(2): p. 233–251.

    Google Scholar 

  24. Erdemir, A., et al., Model-based estimation of muscle forces exerted during movements. Clinical Biomechanics, 2007. 22(2): p. 131–154.

    Google Scholar 

  25. Liu, M.Q., et al., Muscle contributions to support and progression over a range of walking speeds. Journal of Biomechanics, 2008. 41(15): p. 3243–3252.

    Google Scholar 

  26. De Groote, F., et al., Sensitivity of dynamic simulations of gait and dynamometer experiments to hill muscle model parameters of knee flexors and extensors. Journal of biomechanics, 2010. 43(10): p. 1876–1883.

    Google Scholar 

  27. Ashworth, B., Preliminary trial of carisoprodal in multiple sclerosis. Practitioner, 1964. 192: p. 540–542.

    Google Scholar 

  28. Wang, Y., et al., Brain-computer interface based on the high-frequency steady-state visual evoked potential. Proceedings 1st International Conference on Neural Interface and Control Proceedings, 2005.

    Google Scholar 

  29. Manling, H., et al. Application and contrast in brain-computer interface between Hilbert-Huang transform and wavelet transform. in The 9th International Conference for Young Computer Scientists, 2008.

    Google Scholar 

  30. Materka, A., M. Byczuk, and P. Poryzala, A virtual keypad based on alternate half-field stimulated visual evoked potentials. Proceedings International Symposium on Information Technology Convergence, November 23–24, 2007. Jeon Ju, Korea. p. 296–300.

    Google Scholar 

  31. Diez, P., et al., Asynchronous BCI control using high-frequency SSVEP. Journal of Neuroengineering and Rehabilitation, 2011. 8(1): p. 39.

    Google Scholar 

  32. Lynch, D.K. and B.H. Soffer, On the solar spectrum and the color sensitivity of the eye. Optics & Photonics News, 1999. 10(3): p. 28–30.

    Google Scholar 

  33. Ikegami, S., et al., Effect of the green/blue flicker matrix for P300-based brain-computer interface: An EEG-fMRI study. Frontiers in Neurology, 2012. 3(113): p. 1–10.

    Google Scholar 

  34. Soffer, B.H. and D.K. Lynch, Some paradoxes, errors, and resolutions concerning the spectral optimization of human vision. American Association of Physics Teachers, 1999. 67(11): p. 946–953.

    Google Scholar 

  35. Müller-Putz, G.R., et al., Steady-state visual evoked potential (SSVEP)-based communication: Impact of harmonic frequency components. Journal of Neural Engineering, 2005. 2(4): p. 123–130.

    Google Scholar 

  36. Bishop, C.M., Neural Networks for Pattern Recognition. 1995, Oxford: Clarendon Press.

    Google Scholar 

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Author information

Authors and Affiliations

  1. School of Electrical and Electronic Engineering, University of Leeds, Leeds, United Kingdom

    Shane Xie

  2. Department of Mechanical Engineering, The University of Auckland, Auckland, New Zealand

    Shane Xie & Wei Meng

  3. School of Information Engineering, Wuhan University of Technology, Wuhan, China

    Wei Meng

Authors
  1. Shane Xie
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  2. Wei Meng
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Correspondence to Shane Xie .

Editor information

Editors and Affiliations

  1. School of Electrical and Electronic Engineering, University of Leeds, Leeds, United Kingdom

    Shane (S.Q.) Xie

  2. School of Information Engineering, Wuhan University of Technology, Wuhan, Switzerland

    Wei Meng

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Xie, S., Meng, W. (2017). Conclusions and Future Prospects. In: Xie, S., Meng, W. (eds) Biomechatronics in Medical Rehabilitation. Springer, Cham. https://doi.org/10.1007/978-3-319-52884-7_10

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  • DOI: https://doi.org/10.1007/978-3-319-52884-7_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-52883-0

  • Online ISBN: 978-3-319-52884-7

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