IEEE Trans Neural Syst Rehabil Eng. 2020 Jul;28(7):1605-1613. doi: 10.1109/TNSRE.2020.2991643. Epub 2020 May 11.
Limb position is a factor that negatively affects myoelectric pattern recognition classification accuracy. However, prior studies evaluating impact on real-time control for upper-limb amputees have done so without a physical prosthesis on the residual limb. It remains unclear how limb position affects real-time pattern recognition control in amputees when their residual limb is supporting various weights. We used a virtual reality target achievement control test to evaluate the effects of limb position and external load on real-time pattern recognition control in fourteen intact limb subjects and six major upper limb amputee subjects. We also investigated how these effects changed based on different control system training methods. In a static training method, subjects kept their unloaded arm by their side with the elbow bent whereas in the dynamic training method, subjects moved their arm throughout a workspace while supporting a load. When static training was used, limb position significantly affected real-time control in all subjects. However, amputee subjects were still able to adequately complete tasks in all conditions, even in untrained limb positions. Moreover, increasing external loads decreased controller performance, albeit to a lesser extent in amputee subjects. The effects of limb position did not change as load increased, and vice versa. In intact limb subjects, dynamic training significantly reduced the limb position effect but did not completely remove them. In contrast, in amputee subjects, dynamic training eliminated the limb position effect in three out of four outcome measures. However, it did not reduce the effects of load for either subject population. These findings suggest that results obtained from intact limb subjects may not generalize to amputee subjects and that advanced training methods can substantially improve controller robustness to different limb positions regardless of limb loading.
肢体位置是影响肌电模式识别分类准确性的一个因素。然而,之前评估上肢截肢者实时控制影响的研究都是在残肢上没有物理假体的情况下进行的。当残肢承受各种重量时,肢体位置如何影响截肢者的实时模式识别控制尚不清楚。我们使用虚拟现实目标达成控制测试来评估十四名完整肢体受试者和六名主要上肢截肢者受试者的肢体位置和外部负载对实时模式识别控制的影响。我们还研究了这些影响如何基于不同的控制系统训练方法而变化。在静态训练方法中,受试者将未加载的手臂放在身边,肘部弯曲;而在动态训练方法中,受试者在支撑负载的同时在工作空间中移动手臂。当使用静态训练时,肢体位置显著影响所有受试者的实时控制。然而,截肢者受试者仍然能够在所有条件下充分完成任务,即使在未经训练的肢体位置也是如此。此外,增加外部负载会降低控制器性能,但在截肢者受试者中程度较小。肢体位置的影响不会随着负载的增加而改变,反之亦然。在完整肢体受试者中,动态训练显著降低了肢体位置的影响,但并没有完全消除它们。相比之下,在截肢者受试者中,动态训练在四项结果测量中的三项中消除了肢体位置的影响。然而,它并没有减轻对任何受试者群体的负载影响。这些发现表明,从完整肢体受试者获得的结果可能不适用于截肢者受试者,并且先进的训练方法可以大大提高控制器对不同肢体位置的鲁棒性,而与肢体加载无关。