Objective. The appearance of commercial myoelectric armbands has greatly increased the portability and convenience of myoelectric controlled interfaces (MCIs). However, one limitation of the current state-of-the-art myoelectric control algorithms is that they have poor robustness against armband displacements, especially rotation, leading to great algorithmic performance degradation. The traditional remedy, retraining the interface, requires the data collection of all gestures and is impractical in many applications. The recently proposed position verification (PV) framework focused on quickly identifying and correcting the electrode positions after the displacement, showing the potential to restore the performance of MCI in a faster way. However, its online effectiveness is still yet to be validated. Approach. This work proposed a novel algorithm of identifying the rotation direction to improve the efficiency of the PV framework and demonstrated the real-time cap...

中文翻译：

---

基于肌电的手势控制界面的臂章旋转的有效校正。

目的。商业肌电臂带的出现大大增加了肌电控制接口（MCI）的便携性和便利性。但是，当前最新的肌电控制算法的局限性在于，它们对袖带位移（尤其是旋转）的鲁棒性较差，从而导致算法性能大幅下降。对接口进行重新训练的传统补救方法要求收集所有手势的数据，并且在许多应用中不切实际。最近提出的位置验证（PV）框架专注于位移后快速识别和校正电极位置，显示了以更快的方式恢复MCI性能的潜力。但是，其在线有效性仍有待验证。方法。