Improving the Robustness of Real-Time Myoelectric Pattern Recognition against Arm Position Changes in Transradial Amputees.

Previous studies have showed that arm position variations would significantly degrade the classification performance of myoelectric pattern-recognition-based prosthetic control, and the cascade classifier (CC) and multiposition classifier (MPC) have been proposed to minimize such degradation in offline scenarios. However, it remains unknown whether these proposed approaches could also perform well in the clinical use of a multifunctional prosthesis control. In this study, the online effect of arm position variation on motion identification was evaluated by using a motion-test environment (MTE) developed to mimic the real-time control of myoelectric prostheses. The performance of different classifier configurations in reducing the impact of arm position variation was investigated using four real-time metrics based on dataset obtained from transradial amputees. The results of this study showed that, compared to the commonly used motion classification method, the CC and MPC configurations improved the real-time performance across seven classes of movements in five different arm positions (8.7% and 12.7% increments of motion completion rate, resp.). The results also indicated that high offline classification accuracy might not ensure good real-time performance under variable arm positions, which necessitated the investigation of the real-time control performance to gain proper insight on the clinical implementation of EMG-pattern-recognition-based controllers for limb amputees.

先前的研究表明，手臂位置的变化会显著降低基于肌电模式识别的假肢控制的分类性能，并且已经提出了级联分类器（CC）和多位置分类器（MPC）以在离线场景中尽量减少这种性能下降。然而，这些提出的方法在多功能假肢控制的临床使用中是否也能表现良好仍然未知。在这项研究中，通过使用一个为模拟肌电假肢实时控制而开发的运动测试环境（MTE），评估了手臂位置变化对运动识别的在线影响。基于从桡骨截肢者获得的数据，使用四个实时指标研究了不同分类器配置在减少手臂位置变化影响方面的性能。这项研究的结果表明，与常用的运动分类方法相比，CC和MPC配置提高了在五种不同手臂位置下七类运动的实时性能（运动完成率分别提高了8.7%和12.7%）。结果还表明，高的离线分类准确率可能无法确保在手臂位置变化时具有良好的实时性能，这就需要对实时控制性能进行研究，以便对基于肌电模式识别的控制器在肢体截肢者中的临床应用有恰当的了解。