Two ways to improve myoelectric control for a transhumeral amputee after targeted muscle reinnervation: A case study

Yang Xu, Dingguo Zhang, Yang Wang, Juntao Feng, Wendong Xu

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

Background: Myoelectric control of multifunctional prostheses is challenging for individuals with high-level amputations due to insufficient surface electromyography (sEMG) signals. A surgical technique called targeted muscle reinnervation (TMR) has achieved impressive improvements in myoelectric control by providing more sEMG control signals. In this case, some channels of sEMG signals are coupled after TMR, which limits the performance of conventional amplitude-based control for upper-limb prostheses. Methods: In this paper, two different ways (training and algorithms) were attempted to solve the problem in a transhumeral amputee after TMR. Firstly, effect of rehabilitation training on generating independent sEMG signals was investigated. The results indicated that some sEMG signals recorded were still coupled over the targeted muscles after rehabilitation training for about two months. Secondly, pattern recognition (PR) algorithm was then applied to classify the sEMG signals. In the second way, to further improve the real-time performance of prosthetic control, a post-processing method named as mean absolute value-based (MAV-based) threshold switches was utilized. Results: Using the improved algorithms, substantial improvement was shown in a subset of the modified Action Research Arm Test (ARAT). Compared with common PR control without post-processing method, the total scores increased more than 18% with majority vote and more than 58% with MAV-based threshold switches. The amputee was able to finish all the tasks within the allotted time with the standard MAV-based threshold switches. Subjectively the amputee preferred the PR control with MAV-based threshold switches and reported it to be more accurate and much smoother both in experiment and practical use. Conclusions: Although the sEMG signals were still coupled after rehabilitation training on the TMR patient, the online performance of the prosthetic operation was improved through application of PR control with combination of the MAV-based threshold switches. Trial registration: Retrospectively registered http://www.chictr.org.cn/showproj.aspx?proj=22058.

Original languageEnglish
Article number37
Pages (from-to)1-11
Number of pages11
JournalJournal of NeuroEngineering and Rehabilitation
Volume15
Issue number1
DOIs
Publication statusPublished - 10 May 2018

Keywords

  • Myoelectric control
  • Pattern recognition
  • Rehabilitation training
  • Surface electromyography
  • Targeted muscle reinnervation

ASJC Scopus subject areas

  • Rehabilitation
  • Health Informatics

Cite this

Two ways to improve myoelectric control for a transhumeral amputee after targeted muscle reinnervation : A case study. / Xu, Yang; Zhang, Dingguo; Wang, Yang; Feng, Juntao; Xu, Wendong.

In: Journal of NeuroEngineering and Rehabilitation, Vol. 15, No. 1, 37, 10.05.2018, p. 1-11.

Research output: Contribution to journalArticle

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abstract = "Background: Myoelectric control of multifunctional prostheses is challenging for individuals with high-level amputations due to insufficient surface electromyography (sEMG) signals. A surgical technique called targeted muscle reinnervation (TMR) has achieved impressive improvements in myoelectric control by providing more sEMG control signals. In this case, some channels of sEMG signals are coupled after TMR, which limits the performance of conventional amplitude-based control for upper-limb prostheses. Methods: In this paper, two different ways (training and algorithms) were attempted to solve the problem in a transhumeral amputee after TMR. Firstly, effect of rehabilitation training on generating independent sEMG signals was investigated. The results indicated that some sEMG signals recorded were still coupled over the targeted muscles after rehabilitation training for about two months. Secondly, pattern recognition (PR) algorithm was then applied to classify the sEMG signals. In the second way, to further improve the real-time performance of prosthetic control, a post-processing method named as mean absolute value-based (MAV-based) threshold switches was utilized. Results: Using the improved algorithms, substantial improvement was shown in a subset of the modified Action Research Arm Test (ARAT). Compared with common PR control without post-processing method, the total scores increased more than 18{\%} with majority vote and more than 58{\%} with MAV-based threshold switches. The amputee was able to finish all the tasks within the allotted time with the standard MAV-based threshold switches. Subjectively the amputee preferred the PR control with MAV-based threshold switches and reported it to be more accurate and much smoother both in experiment and practical use. Conclusions: Although the sEMG signals were still coupled after rehabilitation training on the TMR patient, the online performance of the prosthetic operation was improved through application of PR control with combination of the MAV-based threshold switches. Trial registration: Retrospectively registered http://www.chictr.org.cn/showproj.aspx?proj=22058.",
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