Abstract
For many centuries, people have speculated that humans could control devices and transfer ideas directly by means of biological signals and without any physical movements. If this could become a reality, it would help the disabled to physically engage with the world. Science fiction has long speculated the use of bio-signals to communicate information between humans and machines. Recent developments in biomechatronics could open a window that allows the brain to directly communicate with the outside world. These developments can potentially bring independence and an improved quality of life to millions of individuals who have mobility impairments.
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References
Population Reference Bureau, 2012 World Population Data Sheet, 2012, Population Reference Bureau: Washington, DC.
Katz, J., et al., A Better Life: What older people with high support needs value, 2011, Joseph Rowntree Foundation: York, UK.
The health success site. Your Health Online_What is Amyotrophic lateral sclerosis (ALS)? 2013 [cited 2013 May]; Available from: http://www.thehealthsuccesssite.com/Your-health-online.html.
Ficke, R.C., Digest of Data on Persons with Disabilities, 1992, Science Management Corp., Washington, DC. p. 207.
NABMRR (National Advisory Board on Medical Rehabilitation Research), Report and Research Plan for the National Center for Medical Rehabilitation Research, 1993, National Institute of Child Health and Human Development, National Institute of Health. p. 63.
Mental Disorders and Illicit Drug Expert Group, New Estimates of Global Burden of Disease Due in 2010. Psychiatric Services, 2008. 59(12): p. 1484–1486.
Carter, G.T., Rehabilitation management in neuromuscular disease. Journal of Neurologic Rehabilitation, 1997. 11(2): p. 69–80.
Fleischer, C. and G. Hommel, A human–exoskeleton interface utilizing electromyography. IEEE Transactions on Robotics, 2008. 24(4): p. 872–882.
Dollar, A.M. and H. Herr, Lower extremity exoskeletons and active orthoses: Challenges and state-of-the-Art. IEEE Transactions on Robotics, 2008. 24(1): p. 144–158.
Kübler, A., et al., Brain-computer communication: Unlocking the locked in. Psychological Bulletin, 2001. 127(3): p. 358–375.
Haynes, J.-D. and G. Rees, Decoding mental states from brain activity in humans. Nature Reviews Neuroscience, 2006. 7(7): p. 523–534.
McDaid, A.J., S. Xing, and S.Q. Xie. Brain controlled robotic exoskeleton for neurorehabilitation. in IEEE/ASME International Conference on Advanced Intelligent Mechatronics, July 9 – 12, 2013. Wollongong, Australia.
Velliste, M., et al., Cortical control of a prosthetic arm for self-feeding. Nature, 2008. 453(7198): p. 1098–1101.
Santhanam, G., et al., A high-performance brain-computer interface. Nature, 2006. 442(7099): p. 195–198.
Chao, Z.C., Y. Nagasaka, and N. Fujii, Long-term asynchronous decoding of arm motion using electrocorticographic signals in monkey. Frontiers in Neuroengineering, 2010. 3.
Marquez-Chin, C., et al., Control of a neuroprosthesis for grasping using off-line classification of electrocorticographic signals: Case study. Spinal Cord, 2009. 47(11): p. 802–808.
University of Pittsburgh School of Medicine. Woman With Quadriplegia Feeds Herself Chocolate Using Mind-Controlled Robot Arm in Pitt/UPMC Study. 2012 [cited 2013 June 20]; Available from: http://upmc.com/media/media-kit/bci/Pages/default.aspx.
Lloyd, D. and T. Buchanan, A model of load sharing between muscles and soft tissues at the human knee during static tasks. Journal of Biomechanical Engineering, 1996. 118(3): p. 367.
Leuthardt, E.C., et al., A brain-computer interface using electrocorticographic signals in humans. Journal of Neural Engineering, 2004. 1(2): p. 63–71.
Margalit, E., et al., Visual and electrical evoked response recorded from subdural electrodes implanted above the visual cortex in normal dogs under two methods of anesthesia. Journal of Neuroscience Methods, 2003. 123(2): p. 129–137.
Kennedy, P.R., et al., Direct control of a computer from the human central nervous system. IEEE Transactions on Rehabilitation Engineering, 2000. 8(2): p. 198–202.
Shain, W., et al., Controlling cellular reactive responses around neural prosthetic devices using peripheral and local intervention strategies. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2003. 11(2): p. 186–188.
Cecotti, H., Spelling with non-invasive Brain–Computer Interfaces – Current and future trends. Journal of Physiology-Paris, 2011. 105(1–3): p. 106–114.
Pilcher, W.H., et al., Intraoperative electrocorticography during tumor Resection - Impact on seizure outcome in patients with gangliogliomas. Journal of Neurosurgery, 1993. 78(6): p. 891–902.
Pfurtscheller, G., B. Graimann, and C. Neuper, EEG-based Brain-Computer Interface System. Wiley Encyclopedia of Biomedical Engineering. 2006: John Wiley & Sons, Inc.
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.
deCharms, R.C., Applications of real-time fMRI. Nature Reviews Neuroscience, 2008. 9(9): p. 720–729.
Mellinger, J., et al., An MEG-based brain-computer interface (BCI). NeuroImage, 2007. 36(3): p. 581–593.
Zoons, E., et al., Structural, functional and molecular imaging of the brain in primary focal dystonia--A review. NeuroImage, 2011. 56(3): p. 1011–1020.
Wallois, F., et al., EEG-NIRS in epilepsy in children and neonates. Neurophysiologie Clinique/Clinical Neurophysiology, 2010. 40(5–6): p. 281–292.
Wolpaw, J. and E.W. Wolpaw, Brain-Computer Interfaces: Principles and Practice. 1 ed. 2012: Oxford University Press.
Wolpaw, J.R., et al., Brain-computer interfaces for communication and control. Clinical Neurophysiology, 2002. 113(6): p. 767–791.
Jasper, H.H., The ten-twenty electrode system of the International Federation. Electroencephalography and Clinical Neurophysiology, 1958. 10(2): p. 371–375.
Binnie, C.D., et al., Practical considerations in the positioning of EEG electrodes. Electroencephalography and Clinical Neurophysiology, 1982. 53(4): p. 453–458.
Rémond, A. and F. Torres, A method of electrode placement with a view to topographical research: I. Basic concepts. Electroencephalography and Clinical Neurophysiology, 1964. 17(5): p. 577–578.
Homan, R.W., J. Herman, and P. Purdy, Cerebral Location of International 10–20 System Electrode Placement. Electroencephalography and Clinical Neurophysiology, 1987. 66(4): p. 376-382.
Lo, H.S. and S.Q. Xie, Exoskeleton robots for upper-limb rehabilitation: State of the art and future prospects. Medical Engineering & Physics, 2012. 34(3): p. 261–268.
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.
Piazza, S.J. and S.L. Delp, The influence of muscles on knee flexion during the swing phase of gait. Journal of Biomechanics, 1996. 29(6): p. 723–733.
Schutte, L.M., et al., Improving the efficacy of electrical stimulation-induced leg cycle ergometry: An analysis based on a dynamic musculoskeletal model. IEEE Transactions on Rehabilitation Engineering, 1993. 1(2): p. 109–125.
Murai, A., et al., Musculoskeletal-see-through mirror: Computational modeling and algorithm for whole-body muscle activity visualization in real time. Progress in Biophysics and Molecular Biology, 2010. 103(2–3): p. 310–317.
Koo, T.K. and A.F. 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.
Rajaratnam, B.S., J.C.H. Goh, and V.P. Kumar, A Comparison of EMG signals from surface and fine-wire electrodes during shoulder abduction. International Journal of Physical Medicine & Rehabilitation, 2014.
Rainoldi, A., G. Melchiorri, and I. Caruso, A method for positioning electrodes during surface EMG recordings in lower limb muscles. Journal of Neuroscience Methods, 2004. 134(1): p. 37–43.
Zheng T., W. Chan Kit, and Y. Hu, A human computer interface drived rehabilitation system for upper limb motion recovery. in IEEE International Conference on Virtual Environments Human-Computer Interfaces and Measurement Systems, July 2 – 4, 2012. p. 26–29.
Watanabe T., et al., Recognition of lower limb movements by artificial neural network for restoring gait of hemiplegic patients by functional electrical stimulation. in Proceedings or the 23rd International Conference of the IEEE Engineering in Medicine and Biology Society, October 25–28, 2002. p. 1348–1351.
He H., et al., Continuous locomotion-mode identification for prosthetic legs based on neuromuscular-mechanical fusion. IEEE Transactions on Biomedical Engineering, 2011. 58(10): p. 2867–2875.
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.
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.
Abdel-Malek K., et al., Optimization-based trajectory planning of the human upper body. Robotica, 2006. 24(6): p. 683–696.
Hill A.V., The Heat of Shortening and the Dynamic Constants of Muscle. Proceedings of the Royal Society of London. Series B, Biological Sciences, 1938. 126(843): p. 136–195.
Hsu W.-H., et al., Differences in torsional joint stiffness of the knee between genders: A human cadaveric study. The American Journal of Sports Medicine, 2006. 34(5): p. 765–770.
Moromugi S., et al., A sensor to measure hardness of human tissue. in IEEE Sensors, October 22 – 25, 2006. Daegu, Korea. p. 388-391.
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.
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.
Oliver N.M., B. Rosario, and A. P. Pentland, A Bayesian computer vision system for modeling human interactions. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000. 22(8): p. 831–843.
Soechting J., and M. Flanders, Evaluating an integrated musculoskeletal model of the human arm. Journal of Biomechanical Engineering, 1997. 119(1): p. 93.
Laursen B., et al., 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.
Lloyd D., and T. Buchanan, A model of load sharing between muscles and soft tissues at the human knee during static tasks. Journal of Biomechanical Engineering, 1996, 118(3): p. 367.
Ferris D.P., et al., An improved powered ankle–foot orthosis using proportional myoelectric control. Gait & Posture, 2006. 23(4): p. 425–428.
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.
M. A. Nussbaum, 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.
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.
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.
Cavallaro F., Fuzzy TOPSIS approach for assessing thermal-energy storage in concentrated solar power (CSP) systems. Applied Energy, 2010. 87(2): p. 496–503.
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Xie, S., Meng, W., Ma, Y. (2017). Introduction. In: Xie, S., Meng, W. (eds) Biomechatronics in Medical Rehabilitation. Springer, Cham. https://doi.org/10.1007/978-3-319-52884-7_1
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DOI: https://doi.org/10.1007/978-3-319-52884-7_1
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