A Hybrid Non-invasive Method for the Classification of Amputee’s Hand and Wrist Movements

  • Oluwarotimi Williams Samuel
  • Xiangxin Li
  • Xu Zhang
  • Hui Wang
  • Guanglin LiEmail author
Conference paper
Part of the IFMBE Proceedings book series (IFMBE, volume 64)


The quest to develop dexterous artificial arm which supports multiple degrees of freedom for amputees has attracted a lot of study interest in the last few decades. The outcome of some of the studies had identified surface Electromyography (EMG) as the most commonly used biological signal in predicting the motion intention of an amputee. Different EMG based control methods for multifunctional prosthesis have been proposed and investigated in a number of previous studies. However, no any multifunctional prostheses are clinically available yet. One of the possible reasons would be that the residual muscles after amputations might not produce sufficient EMG signals for movement classifications. In this study, we proposed to use electroencephalography (EEG) signals recorded from the scalp of an amputee as additional signals for motion identifications. The performance of a hybrid scheme based on the combination of EMG and EEG signals in identifying different hand and wrist movements was evaluated in one transhumeral amputee. Our pilot results showed that the proposed hybrid method increased the classification accuracy in identifying different hand and wrist movements of the amputee. This suggests that the proposed method may have potential to improve the control of multifunctional prostheses.



The authors would like to thank members of the Key Laboratory of Human-Machine Intelligence-Synergy Systems, Chinese Academy of Sciences, Shenzhen Institutes of Advanced Technology, for their assistance in the data acquisition. Lastly, I (O. W. Samuel) sincerely appreciate the support of CAS-TWAS President’s Fellowship to pursue a Ph.D. degree at the University of Chinese Academy of Sciences, Beijing, China. The Research 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, #61203209), and Shenzhen Governmental Basic Research Grant (#JCYJ20130402113127532).


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© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Oluwarotimi Williams Samuel
    • 1
    • 2
  • Xiangxin Li
    • 1
    • 2
  • Xu Zhang
    • 3
  • Hui Wang
    • 2
  • Guanglin Li
    • 1
    Email author
  1. 1.Key Laboratory of Human-Machine Intelligence-Synergy SystemsChinese Academy of Sciences, Shenzhen Institutes of Advanced TechnologyShenzhenChina
  2. 2.Shenzhen College of Advanced TechnologyUniversity of Chinese Academy of SciencesShenzhenChina
  3. 3.Department of BiologySouth University of Science and TechnologyShenzhenChina

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