title: Targeted muscle reinnervation for real-time myoelectric control of multifunction artificial arms.
abstract: BACKGROUND
  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.
  
  
  METHODS
  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.
  
  
  METHODS
  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.
  
  
  CONCLUSIONS
  These results suggest that reinnervated muscles can produce sufficient EMG information for real-time control of advanced artificial arms.
authors.affiliation: Neural Engineering Center for Artificial Limbs, Rehabilitation Institute of Chicago, 345 E Superior St, Room 1309, Chicago, IL 60611, USA. tkuiken@northwestern.edu
authors.forename: Todd A
authors.initials: TA
authors.lastname: Kuiken
authors.forename: Guanglin
authors.initials: G
authors.lastname: Li
authors.forename: Blair A
authors.initials: BA
authors.lastname: Lock
authors.forename: Robert D
authors.initials: RD
authors.lastname: Lipschutz
authors.forename: Laura A
authors.initials: LA
authors.lastname: Miller
authors.forename: Kathy A
authors.initials: KA
authors.lastname: Stubblefield
authors.forename: Kevin B
authors.initials: KB
authors.lastname: Englehart
mesh_terms: D000328:Adult; D000671:Amputation, Surgical / Q000379:methods*; D000672:Amputation Stumps / Q000294:innervation*; D001132:Arm / Q000294:innervation*; D001186:Artificial Limbs; D004576:Electromyography; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D009068:Movement; D018482:Muscle, Skeletal / Q000294:innervation*; D016067:Nerve Transfer; D010363:Pattern Recognition, Automated; D011474:Prosthesis Design; D055815:Young Adult