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.

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了解肢体位置和外部负载对截肢者实时模式识别控制的影响

肢体位置是负面影响肌电模式识别分类准确性的因素。但是，先前的评估对上肢截肢者实时控制的影响的研究没有在残肢上安装假体。当截肢者的残肢支撑着各种重量时，肢体位置如何影响实时模式识别控制尚不清楚。我们使用虚拟现实目标成就控制测试来评估肢体位置和外部负荷对14个完整肢体受试者和6个主要上肢截肢者受试者的实时模式识别控制的影响。我们还研究了基于不同控制系统训练方法的这些影响如何变化。在静态训练方法中 受测者肘部弯曲时将手臂保持在旁边，而在动态训练方法中，受测者在支撑负荷的同时将手臂移动到整个工作空间。当使用静态训练时，四肢的位置会显着影响所有受试者的实时控制。但是，即使在未经训练的肢体姿势下，被截肢者仍然能够在所有条件下充分完成任务。此外，增加外部负载会降低控制器的性能，尽管在被截肢者中程度较小。肢体位置的影响不会随着负荷增加而改变，反之亦然。在完整的肢体受试者中，动态训练显着降低了肢体位置的影响，但并未完全消除它们。相反，在被截肢者中，动态训练消除了四分之三的测量结果中肢体位置的影响。但是，它并没有减少负荷对这两个受试者群体的影响。这些发现表明，从完整肢体受试者获得的结果可能不会推广到截肢受试者，并且先进的训练方法可以显着提高控制器对不同肢体位置的鲁棒性，而与肢体负荷无关。