Kuiken Todd A, Li Guanglin, Lock Blair A, Lipschutz Robert D, Miller Laura A, Stubblefield Kathy A, Englehart Kevin B
Neural Engineering Center for Artificial Limbs, Rehabilitation Institute of Chicago, 345 E Superior St, Room 1309, Chicago, IL 60611, USA.
JAMA. 2009 Feb 11;301(6):619-28. doi: 10.1001/jama.2009.116.
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.
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.
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.
改善假肢手臂的功能仍然是一项挑战，因为在截肢过程中会失去手臂的神经控制信息。一种名为靶向肌肉再支配（TMR）的外科技术将手臂残余神经转移到其他肌肉部位。再支配后，这些目标肌肉会在皮肤表面产生肌电图（EMG）信号，这些信号可以被测量并用于控制假肢手臂。
使用模式识别算法解码EMG信号并控制假肢手臂运动，评估接受TMR手术的上肢截肢患者的表现。
设计、地点和参与者：2007年1月至2008年1月在芝加哥康复研究所对5例在2002年2月至2006年10月期间接受TMR手术的肩关节离断或经肱骨截肢患者以及5例无截肢的对照参与者进行了研究。记录了所有参与者的表面EMG信号，并使用模式识别算法进行解码。解码程序控制虚拟假肢手臂的运动。所有参与者均被指示进行各种手臂运动，并测量他们控制虚拟假肢手臂的能力。此外，TMR患者使用相同的控制系统操作先进的手臂假肢原型。
虚拟手臂运动期间测量的性能指标包括运动选择时间、运动完成时间和运动完成（“成功”）率。
TMR患者能够使用虚拟假肢手臂反复进行10种不同的肘部、腕部和手部运动。对于这些患者，肘部和腕部运动的平均运动选择时间和运动完成时间分别为0.22秒（标准差，0.06）和1.29秒（标准差，0.15）。这些时间比对照参与者的平均时间长0.06秒和0.21秒。对于TMR患者，手部抓握模式的平均运动选择时间和运动完成时间分别为0.38秒（标准差，0.12）和1.54秒（标准差，0.27）。这些患者在5秒内成功完成了平均96.3%（标准差，3.8）的肘部和腕部运动以及86.9%（标准差，13.9）的手部运动，而对照组分别为100%（标准差，0）和96.7%（标准差，4.7）。其中3名患者能够展示在先进假肢中使用这种控制系统，包括电动肩部、肘部、腕部和手部。
这些结果表明，再支配的肌肉可以产生足够的EMG信息用于实时控制先进的人造手臂。
