Efficient correction of armband rotation for myoelectric-based gesture control interface

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.

This work proposed a novel algorithm of identifying the rotation direction to improve the efficiency of the PV framework and demonstrated the real-time capability of the PV framework using a commercially available armband.

The results showed that with PV, a 1.5-cm rotation could be corrected with an average of 3.1 ± 1.5 interactive adjustments, equivalent to around 15.5 ± 7.5 s, which was greatly reduced compared to retraining. There was no significant difference in the real-time control performance between before the armband displacement and after the PV correction.

To the best of our knowledge, this study was the first maintaining pattern recognition-based myoelectric control performance in the presence of electrode shifts without recollecting the entire training data. It suggested the feasibility of the PV framework used in the myoelectric armband and MCI for practical applications.