Targeted Muscle Reinnervation for Real-Time Myoelectric Control of Multifunction Artificial Arms

By Todd A. Kuiken, Guanglin Li, Blair A. Lock, Robert D. Lipschutz, L. Miller, K. Stubblefield, Kevin B. Englehart. Journal of the American Medical Association (JAMA), 2009

928 citations recorded by the source corpus.

Context Improving the function of prosthetic arms remains a challenge, as access to the neural control information for the arm is lost during amputation. We have developed a surgical technique called targeted muscle reinnervation (TMR) which transfers residual arm nerves to alternative muscle sites. After reinnervation, these target muscles produce an electromyogram (EMG) on the surface of the skin that can be measured and used to control prosthetic arms. Objective Assess the performance of TMR upper-limb amputee patients using a pattern-recognition algorithm to decode EMG signals and control prosthetic arm motions. Design Surface EMG signals were recorded on participants and decoded using a pattern-recognition algorithm. The decoding program controlled the movement of a virtual prosthetic arm. 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. Setting This study was conducted between January 2007 and January 2008 at the Rehabilitation Institute of Chicago. Participants This study included five patients with shoulder disarticulation or transhumeral amputations who received TMR surgery between February 2002 and October 2006. It also included five non-amputee (control) participants. Main Outcome Measure Performance metrics measured during virtual arm movements included motion-selection time, motion-completion time, and motion-completion (or `success') rate. Three of the TMR patients were also able to test advanced arm prostheses. Results TMR patients were able to repeatedly perform 10 different elbow, wrist and hand motions with the virtual prosthetic arm. For TMR patients, the average (standard deviation (SD)) motion-selection and motion-completion times for elbow and wrist movements were 0.22 s (0.06) and 1.29 s (0.15), respectively. These times were 0.06 s and 0.21 s longer than the average times of control participants. For TMR patients, the average (SD) motion-selection and motion-completion times for hand-grasp patterns were 0.38 s (0.12) and 1.54 s (0.27), respectively. TMR patients successfully completed an average (SD) of 96.3% (3.8) of elbow and wrist movements and 86.9% (13.9) of hand movements within 5 s, compared to 100% (0) and 96.7% (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. Conclusion These results suggest that reinnervated muscles can produce sufficient EMG information to control advanced artificial arms.

Subfields: Biomedical Engineering, Cellular and Molecular Neuroscience, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/4c855721e0a6e8c40da5070ea10f6e116a6db124

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Citations in the corpus

Real-Time EMG Based Pattern Recognition Control for Hand Prostheses: A Review on Existing Methods, Challenges and Future Implementation (2019, methodology): “N/A 150 ms analysis window with 50 ms overlap MAV, ZC, SSC, WL LDA [86] N/A 11/12 1 kHz USB-1616FS”
Autonomy in Rehabilitation Robotics: An Intersection (2018, neutral): “Recent work is beginning to explore algorithms that 6An additional avenue is to modify the human body itself, rather than the machine side of the interface—as is done in the TMR surgery mentioned in Section 2.2. www.annualreviews.org • Autonomy in Rehabilitation Robotics 9 perceive the external worl”
Electromyogram pattern recognition for control of powered upper-limb prostheses: state of the art and challenges for clinical use. (2011, background): “This approach has been shown to perform very well in real-time [12] with both shoulder disarticulation and transhumeral amputees, and clinical experience with pattern recognition and TMR shows great promise.”
Resolving the Limb Position Effect in Myoelectric Pattern Recognition (2011, background): “[23] introduced alternative quantitative usability metrics focused on class selection and motion completion times.”
Design of a highly biomimetic anthropomorphic robotic hand towards artificial limb regeneration (2016, methodology): “…Using non-intrusive methods such as electromyography (EMG) signals collected from the residual limb or targeted muscle reinnervation regions [4]; or using intrusive methods like directly implanting microelectrodes at the motor cortex of the brain [5] or cuffing peripheral nerves with…”
Long-term stability of neural prosthetic control signals from silicon cortical arrays in rhesus macaque motor cortex (2011, background): “The clinical need for such control signals is high, since there is a substantial population of paralyzed patients, and state-of-theart prosthetic limbs currently have more degrees of freedom than one can easily control with conventional approaches [19].”
Deep Learning with Convolutional Neural Networks Applied to Electromyography Data: A Resource for the Classification of Movements for Prosthetic Hands (2016, methodology): “Promising results have been obtained by invasive methods such as Peripheral Nerve Interfaces (Urbanchek et al., 2012), Cortical Interfaces (Chestek et al., 2011) or Targeted Muscle Reinnervation (TMR; Kuiken et al., 2009).”
The Extraction of Neural Information from the Surface EMG for the Control of Upper-Limb Prostheses: Emerging Avenues and Challenges (2014, background): “The notion of surface EMG recordings as sources of neural information is emphasized by the emerging procedure of targeted muscle reinnervation (TMR), that redirects nerves that would reach the missing limb muscles to innervate accessory muscles, from which surface EMG is recorded and the motion inte”