Abstract
In the last years there has been a noticeable progress in motor learning, neuroplasticity and functional recovery after the occurrence of brain lesion. Rehabilitation of motor function has been associated to motor learning that occurs during repetitive, frequent and intensive training. Neuro-rehabilitation is based on the assumption that motor learning principles can be applied to motor recovery after injury, and that training can lead to permanent improvements of motor functions in patients with muscle deficits. The emergent research field of Rehabilitation Engineering may provide promise technologies for neuro-rehabilitation therapies, exploiting the motor learning and neural plasticity concepts. Among those technologies, the FES-assisted systems could provide repetitive training-based therapies and have been developed to aid or control the upper and lower limbs movements in response to user’s intentionality. Surface electromyography (SEMG) reflects directly the human motion intention, so it can be used as input information to control an active FES-assisted system. The present work describes a neurorehabilitation platform at the upper-limb level, based on bilateral coordination training (i.e. mirror movements with the unaffected arm) using a close-loop active FES system controlled by user. In this way, this work presents a novel myoelectric controller for decoding movements of user to be employed in a neurorehabilitation platform. It was carried out a set of experiments to validate the myoelectric controller in classification of seven human upper-limb movements, obtaining an average classification error of 4.3%. The results suggest that the proposed myoelectric pattern recognition method may be applied to control close-loop FES system.
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References
Dietz, V., Nef, T., Rymer, W.Z.: Neurorehabilitation technology. Springer (2012)
Sharma, N., Classen, J., Cohen, L.G.: Neural plasticity and its contribution to functional recovery. In: Handbook of Clinical Neurology, vol. 110, pp. 3–12 (2013)
Moller, A.R.: Neural plasticity and disorders of the nervous system. Cambridge University Press (2006)
Hara, Y., Obayashi, S., Tsujiuchi, K., Muraoka, Y.: The effects of electromyography-controlled functional electrical stimulation on upper extremity function and cortical perfusion in stroke patients. Clinical Neurophysiology 124, 2008–2015 (2013)
Sheffler, L., Chae, J.: Neuromuscular electrical stimulation in neurorehabilitation. Muscle Nerve 35, 562–590 (2007)
Doucet, B.M., Lamb, A., Griffin, L.: Neuromuscular electrical stimulation for skeletal muscle function. The Yale Journal of Biology and Medicine 85, 201–215 (2012)
Hamid, S., Hayek, R.: Role of electrical stimulation for rehabilitation and regeneration after spinal cord injury: an overview. Eur. Spine J. 17, 1256–1269 (2008)
Sabut, S.K., Sikdar, C., Mondal, R., Kumar, R., Mahadevappa, M.: Restoration of gait and motor recovery by functional electrical stimulation therapy in persons with stroke. Disability and Rehabilitation 32(19), 1594–1603 (2010)
Popovic, L., Jorgovanovic, N., Ilic, V., Dosen, S., Keller, T., Popovic, M.B., Popovic, D.B.: Electrical stimulation for the suppression of pathological tremor. Med. Biol. Eng. Comput. 49, 1187–1193 (2011)
Wolczowski, A., Kurzynski, M.: Human-machine interface in bioprosthesis control using EMG signal classification. Expert Systems 27, 53–70 (2010)
Farina, D., Negro, F.: Accessing the neural drive to muscle and translation to neurorehabilitation technologies. IEEE Reviews in Biomedical Engineering 5, 3–14 (2012)
Oskoei, M.A., Hu, H.: Myoelectric control systems–A survey. Biomedical Signal Processing and Control 2, 275–294 (2007)
Jamison, J.C., Caldwell, G.E.: Muscle synergies and isometric torque production: Influence of supination and pronation level on elbow flexion. J. Neurophys. 70(3), 947–960 (1993)
Englehart, K., Hudgins, B.: A robust real-time control scheme for multifunctionmyoelectric control. IEEE Trans. Biomed. Eng. 50(7), 848–854 (2003)
Tarkka, I.M., Pitkanen, K., Popovic, D.J., Vanninen, R., Kononen, M.: Functional Electrical Therapy for Hemiparesis Alleviates Disability and Enhances Neuroplasticity. J. Exp. Med. 225, 71–76 (2011)
Kimberley, T.J., Lewis, S.M., Auerbach, E.J., Dorsey, L.L., Lojovich, J.M., Carey, J.R.: Electrical stimulation driving functional improvements and cortical changes in subjects with stroke. Experimental Brain Research 154, 450–460 (2004)
Shin, H.K., Cho, S.H., Jeon, H.S., Lee, Y.H., Song, J.C., Jang, S.H., Lee, C.H., Kwon, Y.H.: Cortical effect and functional recovery by the electromyography-triggered neuromuscular stimulation in chronic stroke patients. Neuroscience Letters 442, 174–179 (2008)
Sheffler, L., Chae, J.: Technological Advances in Interventions to Enhance Poststroke Gait. Phys. Med. Rehabil. Clin. N. Am. 24, 305–323 (2013)
Cauraugh, J.H., Kim, S.: Two Coupled Motor Recovery Protocols Are Better Than One: Electromyogram-Triggered Neuromuscular Stimulation and Bilateral Movements. Stroke 33, 1589–1594 (2002)
Stewart, K.C., Cauraugh, J.H., Summers, J.J.: Bilateral movement training and stroke rehabilitation: A systematic review and meta-analysis. Journal of the Neurological Sciences 244, 89–95 (2006)
Knutson, J.S., Harley, M.Y., Hisel, T.Z., Makowski, N.Z., Fu, M.J., Chae, J.: Contralaterally Controlled Functional Electrical Stimulation for Stroke Rehabilitation. In: Conf. Proc. IEEE Eng. Med. Biol. Soc., pp. 314–317 (August 2012)
Surface electromyography for the non-invasive assessment of muscles project. web page, http://seniam.org/
Huang, Y.H., Englehart, K., Hudgins, B.S., Chan, A.D.C.: A Gaussian mixture model based classification scheme for myoelectric control of powered upper limb prostheses. IEEE Trans. Biomed. Eng. 52(11), 1801–1811 (2005)
Phinyomark, A., Phukpattaranont, P., Limsakul, C.: Feature reduction and selection for EMG signal classification. Expert Systems with Applications 39, 7420–7431 (2012)
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Ruiz-Olaya, A.F., López-Delis, A., Cerquera, A. (2015). Toward an Upper-Limb Neurorehabilitation Platform Based on FES-Assisted Bilateral Movement: Decoding User’s Intentionality. In: Ferrández Vicente, J., Álvarez-Sánchez, J., de la Paz López, F., Toledo-Moreo, F., Adeli, H. (eds) Artificial Computation in Biology and Medicine. IWINAC 2015. Lecture Notes in Computer Science(), vol 9107. Springer, Cham. https://doi.org/10.1007/978-3-319-18914-7_15
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DOI: https://doi.org/10.1007/978-3-319-18914-7_15
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