Two ways to improve myoelectric control for a transhumeral amputee after targeted muscle reinnervation: a case study
- PDF / 1,919,026 Bytes
- 11 Pages / 595 x 791 pts Page_size
- 3 Downloads / 126 Views
RESEARCH
Open Access
Two ways to improve myoelectric control for a transhumeral amputee after targeted muscle reinnervation: a case study Yang Xu1 , Dingguo Zhang1* , Yang Wang1 , Juntao Feng2 and Wendong Xu2
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 ChiCTR-ONC-17014163. Keywords: Surface electromyography, Targeted muscle reinnervation, Rehabilitation training, Myoelectric control, Pattern recognition Background Myoelectric upper-limb prostheses where surface electromyography (sEMG) signals are utilized as control sources have been developed for a long time [1, 2]. However, control of a prosthesis with multiple degrees of freedom (DoFs) including elbow, wrist and hand for *Correspondence: [email protected] State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Dongchuan Road, 200240 Shanghai, China Full list of author information is available at the end of the article 1
high-level amputees is still difficult due to la
Data Loading...
