Abstract
Limb-amputation, stroke, trauma, and some other congenital anomalies not only decrease patients’ quality of life but also cause severe psychological burdens to them. Several advanced rehabilitation technologies have been developed to help patients with limb disabilities restore their lost motor functions. As a kind of neural signal, surface electromyogram (sEMG) recorded on limb muscles usually contain rich information associated with limb motions. By decoding the sEMG with pattern recognition techniques, the motion intents can be effectively identified and used for the control of rehabilitation devices. In this chapter, the control of upper-limb prostheses and rehabilitation robots based on the pattern recognition of sEMG signals was detailedly introduced and discussed. In addition, the clinical feasibility of sEMG-based pattern recognition technique towards an improved function restoration for upper-limb amputees and stroke survivors is also described.
Keywords
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Acknowledgements
The work was supported in part by the National Key Basic Research Program of China (2013CB329505), the National Natural Science Foundation of China under Grants (#61135004, #91420301, #61203209, #61403367), the National High Technology Research and Development Program of China (#2105AA042303), the Natural Science Foundation for Distinguished Young Scholars of Guangdong Province, China (2014A030306029), the Special Support Program for Eminent Professionals of Guangdong Province, China (2015TQ01C399), and the Shenzhen Peacock Plan Grant (#KQCX2015033117354152, #JCYJ20150401145529005). Lastly, I (O.W. Samuel) sincerely appreciate the support of CAS-TWAS President’s Fellowship in the pursuit of a Ph.D. degree at the University of Chinese Academy of Sciences, Beijing, China.
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Samuel, O.W., Fang, P., Chen, S., Geng, Y., Li, G. (2017). Activity Recognition Based on Pattern Recognition of Myoelectric Signals for Rehabilitation. In: Khan, S., Zomaya, A., Abbas, A. (eds) Handbook of Large-Scale Distributed Computing in Smart Healthcare. Scalable Computing and Communications. Springer, Cham. https://doi.org/10.1007/978-3-319-58280-1_16
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DOI: https://doi.org/10.1007/978-3-319-58280-1_16
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