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

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.

Figure 1

Schematic of TMR surgery in a participants S1 (a), S2 (b) and S3 (c).

Figure 2

Timeline of relevant history of TMR patients including original amputation, TMR surgery, practice with the virtual arm, and testing for this study.

Figure 3

(a) Five hand-grasp patterns used in this study. (b) Screen shot of the prompted movement and responding virtual arm.

Figure 4

Two performance metrics: motion-selection time (MS_t) and motion-completion time (MC_t). The target motion classes are shown by green dots and the decisions of the classifier are depicted as blue circles. Each target movement started from a state of rest. The classifier made a motion prediction every 100 ms. The mean absolute values of the recorded EMG signals are shown in the bottom panel.

Figure 5

(a) Motion-selection time histogram for both TMR patients and control participants. (b) Motion-completion time histogram for both TMR patients and control participants. Both times were calculated from all the completed movements with a time limit of 5 s. The time bin is 0.1 s for motion-selection times and 0.5 s for motion-completion times.

Figure 6

Completion rate vs. time. (a) Elbow and wrist completion rates for the five TMR patients. (b) Hand-grasp completion rates for the five TMR patients. (c) Average completion rates for TMR patients (black lines) and control participants (red lines), respectively.

Figure 7

Patients using experimental arm prostheses. (a) Patient S2 was shown reaching to catch a tissue box using the DEKA arm. (b) Patient S1 was shown moving a ring across a geometric wire using the JHUAPL arm. (c) Patient T5 was shown grabbing a pen using the DEKA arm.

Figure 7

Patients using experimental arm prostheses. (a) Patient S2 was shown reaching to catch a tissue box using the DEKA arm. (b) Patient S1 was shown moving a ring across a geometric wire using the JHUAPL arm. (c) Patient T5 was shown grabbing a pen using the DEKA arm.

Figure 7

Patients using experimental arm prostheses. (a) Patient S2 was shown reaching to catch a tissue box using the DEKA arm. (b) Patient S1 was shown moving a ring across a geometric wire using the JHUAPL arm. (c) Patient T5 was shown grabbing a pen using the DEKA arm.