Targeted Muscle Reinnervation for Real-time Myoelectric Control of Multifunction Artificial Arms | Medical Devices and Equipment | JAMA | JAMA Network
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Original Contribution
February 11, 2009

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

Author Affiliations

Author Affiliations: Neural Engineering Center for Artificial Limbs, Rehabilitation Institute of Chicago, Chicago, Illinois (Drs Kuiken, Li, and Miller, Mssrs Lock and Lipschutz, and Ms Stubblefield); Departments of Physical Medicine and Rehabilitation (Drs Kuiken, Li, and Miller) and Biomedical Engineering, Northwestern University, Chicago, Illinois (Dr Kuiken); and Institute of Biomedical Engineering, University of New Brunswick, Fredericton, New Brunswick, Canada (Dr Englehart).

JAMA. 2009;301(6):619-628. doi:10.1001/jama.2009.116
Abstract

Context 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.

Objective 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.

Design, Setting, and Participants 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.

Main Outcome Measure Performance metrics measured during virtual arm movements included motion selection time, motion completion time, and motion completion (“success”) rate.

Results 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.

Conclusion These results suggest that reinnervated muscles can produce sufficient EMG information for real-time control of advanced artificial arms.

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