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

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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.

Electronic supplementary material

The online version of this article (10.1186/s12984-018-0376-9) contains supplementary material, which is available to authorized users.

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Most cited references 33

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High-performance neuroprosthetic control by an individual with tetraplegia.

Jennifer L. Collinger, Brian Wodlinger, John Downey … (2013)

Paralysis or amputation of an arm results in the loss of the ability to orient the hand and grasp, manipulate, and carry objects, functions that are essential for activities of daily living. Brain-machine interfaces could provide a solution to restoring many of these lost functions. We therefore tested whether an individual with tetraplegia could rapidly achieve neurological control of a high-performance prosthetic limb using this type of an interface. We implanted two 96-channel intracortical microelectrodes in the motor cortex of a 52-year-old individual with tetraplegia. Brain-machine-interface training was done for 13 weeks with the goal of controlling an anthropomorphic prosthetic limb with seven degrees of freedom (three-dimensional translation, three-dimensional orientation, one-dimensional grasping). The participant's ability to control the prosthetic limb was assessed with clinical measures of upper limb function. This study is registered with ClinicalTrials.gov, NCT01364480. The participant was able to move the prosthetic limb freely in the three-dimensional workspace on the second day of training. After 13 weeks, robust seven-dimensional movements were performed routinely. Mean success rate on target-based reaching tasks was 91·6% (SD 4·4) versus median chance level 6·2% (95% CI 2·0-15·3). Improvements were seen in completion time (decreased from a mean of 148 s [SD 60] to 112 s [6]) and path efficiency (increased from 0·30 [0·04] to 0·38 [0·02]). The participant was also able to use the prosthetic limb to do skilful and coordinated reach and grasp movements that resulted in clinically significant gains in tests of upper limb function. No adverse events were reported. With continued development of neuroprosthetic limbs, individuals with long-term paralysis could recover the natural and intuitive command signals for hand placement, orientation, and reaching, allowing them to perform activities of daily living. Defense Advanced Research Projects Agency, National Institutes of Health, Department of Veterans Affairs, and UPMC Rehabilitation Institute. Copyright © 2013 Elsevier Ltd. All rights reserved.

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A robust, real-time control scheme for multifunction myoelectric control.

K Englehart, B Hudgins (2003)

This paper represents an ongoing investigation of dexterous and natural control of upper extremity prostheses using the myoelectric signal (MES). The scheme described within uses pattern recognition to process four channels of MES, with the task of discriminating multiple classes of limb movement. The method does not require segmentation of the MES data, allowing a continuous stream of class decisions to be delivered to a prosthetic device. It is shown in this paper that, by exploiting the processing power inherent in current computing systems, substantial gains in classifier accuracy and response time are possible. Other important characteristics for prosthetic control systems are met as well. Due to the fact that the classifier learns the muscle activation patterns for each desired class for each individual, a natural control actuation results. The continuous decision stream allows complex sequences of manipulation involving multiple joints to be performed without interruption. Finally, minimal storage capacity is required, which is an important factor in embedded control systems.

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Targeted muscle reinnervation for real-time myoelectric control of multifunction artificial arms.

Todd Kuiken, Guanglin Li, Blair A. Lock … (2009)

Improving the function of prosthetic arms remains a challenge, because access to the neural-control information for the arm is lost during amputation. A surgical technique called targeted muscle reinnervation (TMR) transfers residual arm nerves to alternative muscle sites. After reinnervation, these target muscles produce electromyogram (EMG) signals on the surface of the skin that can be measured and used to control prosthetic arms. To assess the performance of patients with upper-limb amputation who had undergone TMR surgery, using a pattern-recognition algorithm to decode EMG signals and control prosthetic-arm motions. Study conducted between January 2007 and January 2008 at the Rehabilitation Institute of Chicago among 5 patients with shoulder-disarticulation or transhumeral amputations who underwent TMR surgery between February 2002 and October 2006 and 5 control participants without amputation. Surface EMG signals were recorded from all participants and decoded using a pattern-recognition algorithm. The decoding program controlled the movement of a virtual prosthetic arm. All participants were instructed to perform various arm movements, and their abilities to control the virtual prosthetic arm were measured. In addition, TMR patients used the same control system to operate advanced arm prosthesis prototypes. Performance metrics measured during virtual arm movements included motion selection time, motion completion time, and motion completion ("success") rate. The TMR patients were able to repeatedly perform 10 different elbow, wrist, and hand motions with the virtual prosthetic arm. For these patients, the mean motion selection and motion completion times for elbow and wrist movements were 0.22 seconds (SD, 0.06) and 1.29 seconds (SD, 0.15), respectively. These times were 0.06 seconds and 0.21 seconds longer than the mean times for control participants. For TMR patients, the mean motion selection and motion completion times for hand-grasp patterns were 0.38 seconds (SD, 0.12) and 1.54 seconds (SD, 0.27), respectively. These patients successfully completed a mean of 96.3% (SD, 3.8) of elbow and wrist movements and 86.9% (SD, 13.9) of hand movements within 5 seconds, compared with 100% (SD, 0) and 96.7% (SD, 4.7) completed by controls. Three of the patients were able to demonstrate the use of this control system in advanced prostheses, including motorized shoulders, elbows, wrists, and hands. These results suggest that reinnervated muscles can produce sufficient EMG information for real-time control of advanced artificial arms.

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Author and article information

Contributors

Yang Xu: yangeksu@sjtu.edu.cn

Dingguo Zhang: dgzhang@sjtu.edu.cn

Yang Wang: maibiantianxia@163.com

Juntao Feng: jtfeng08@fudan.edu.cn

Wendong Xu: wendongxu@fudan.edu.cn

Journal

Journal ID (nlm-ta): J Neuroeng Rehabil

Journal ID (iso-abbrev): J Neuroeng Rehabil

Title: Journal of NeuroEngineering and Rehabilitation

Publisher: BioMed Central (London )

ISSN (Electronic): 1743-0003

Publication date (Electronic): 10 May 2018

Publication date PMC-release: 10 May 2018

Publication date Collection: 2018

Volume: 15

Electronic Location Identifier: 37

Affiliations

[1 ]ISNI 0000 0004 0368 8293, GRID grid.16821.3c, State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, ; Dongchuan Road, Shanghai, 200240 China

[2 ]Department of Hand Surgery, Huashan Hospital, Fudan University, Wulumuqi Road, Shanghai, 200040 China

Article

Publisher ID: 376

DOI: 10.1186/s12984-018-0376-9

PMC ID: 5946536

PubMed ID: 29747672

SO-VID: c5891605-de57-4901-9411-b2245e96de6d

License:

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

History

Date received : 9 October 2017

Date accepted : 13 April 2018

Funding

Funded by: National High Technology Research and Development Program (863 Program) of China

Award ID: 2015AA020501

Custom metadata

ScienceOpen disciplines: Neurosciences

Keywords: surface electromyography,targeted muscle reinnervation,rehabilitation training,myoelectric control,pattern recognition

Data availability:

ScienceOpen disciplines: Neurosciences

Keywords: surface electromyography, targeted muscle reinnervation, rehabilitation training, myoelectric control, pattern recognition

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

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Most cited references 33

High-performance neuroprosthetic control by an individual with tetraplegia.

A robust, real-time control scheme for multifunction myoelectric control.

Targeted muscle reinnervation for real-time myoelectric control of multifunction artificial arms.

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