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Activity Recognition Based on Pattern Recognition of Myoelectric Signals for Rehabilitation

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Handbook of Large-Scale Distributed Computing in Smart Healthcare

Part of the book series: Scalable Computing and Communications ((SCC))

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.

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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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Authors and Affiliations

  1. Chinese Academy of Sciences (CAS), Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology (SIAT), Shenzhen, 518055, China

    Oluwarotimi Williams Samuel, Peng Fang, Shixiong Chen, Yanjuan Geng & Guanglin Li

  2. The Institute of Biomedical and Health Engineering, SIAT, Shenzhen, 518055, China

    Oluwarotimi Williams Samuel, Peng Fang, Shixiong Chen, Yanjuan Geng & Guanglin Li

  3. Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, 518055, China

    Oluwarotimi Williams Samuel, Peng Fang, Shixiong Chen & Guanglin Li

  4. 1068 Xueyuan Avenue, University Town, Nanshan, Shenzhen, 518055, China

    Guanglin Li

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  1. Oluwarotimi Williams Samuel
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Correspondence to Guanglin Li .

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Editors and Affiliations

  1. Department of Electrical and Computer Engineering, North Dakota State University , Fargo, North Dakota, USA

    Samee U. Khan

  2. School of Information Technologies, University of Sydney, Sydney, New South Wales, Australia

    Albert Y. Zomaya

  3. Department of Computer Science, COMSATS Institute of Information Technology, Islamabad, Pakistan

    Assad Abbas

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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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