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Realizing Efficient EMG-Based Prosthetic Control Strategy

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Neural Interface: Frontiers and Applications

Part of the book series: Advances in Experimental Medicine and Biology ((AEMB,volume 1101))

Abstract

As an integral part of the body, the limb poses dexterous and fine motor gras** and sensing capabilities that enable humans to effectively communicate with their environment during activities of daily living (ADL). Hence, limb loss severely limits individuals’ ability especially when they need to perform tasks requiring their limb functions during ADL, thus leading to decreased quality of life. To effectively restore limb functions in amputees, the advanced prostheses that are controlled by electromyography (EMG) signal have been widely investigated and used. Since EMG signals reflect neural activity, they would contain information on the muscle activation related to limb motions. Pattern recognition-based myoelectric control is an important branch of the EMG-based prosthetic control. And the EMG-based prosthetic control theoretically supports multiple degrees of freedom movements  that allows amputees to intuitively manipulate the device. This chapter focuses on EMG-based prosthetic control strategy that involves utilizing intelligent computational technique to decode upper limb movement intentions from which control commands are derived. Additionally, different techniques/methods for improving the overall performance of EMG-based prostheses control strategy were introduced and discussed in this chapter.

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Acknowledgments

This work was supported in part by the National Natural Science Foundation of China (#U1613222, #81850410557), the Shenzhen Basic Research Grant (#JCYJ20160331185848286), and the Outstanding Youth Innovation Research Fund of Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences (#Y7G016).

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Authors and Affiliations

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

    Guanglin Li, Oluwarotimi Williams Samuel, Chuang Lin, Mojisola Grace Asogbon, Peng Fang & Paul Oluwagbengba Idowu

  2. Institute of Advanced Integration Technology, SIAT, Shenzhen, China

    Guanglin Li, Oluwarotimi Williams Samuel, Chuang Lin, Mojisola Grace Asogbon, Peng Fang & Paul Oluwagbengba Idowu

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

    Mojisola Grace Asogbon & Paul Oluwagbengba Idowu

Authors
  1. Guanglin Li
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  2. Oluwarotimi Williams Samuel
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  3. Chuang Lin
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  4. Mojisola Grace Asogbon
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  5. Peng Fang
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  6. Paul Oluwagbengba Idowu
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Correspondence to Guanglin Li .

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Editors and Affiliations

  1. Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou, Zhejiang, China

    **aoxiang Zheng

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Li, G., Samuel, O.W., Lin, C., Asogbon, M.G., Fang, P., Idowu, P.O. (2019). Realizing Efficient EMG-Based Prosthetic Control Strategy. In: Zheng, X. (eds) Neural Interface: Frontiers and Applications. Advances in Experimental Medicine and Biology, vol 1101. Springer, Singapore. https://doi.org/10.1007/978-981-13-2050-7_6

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