Effects of Electrode Position Targeting in Noninvasive Electromyography Technologies for Finger and Hand Movement Prediction

Abstract

Purpose: Stroke patients may need to undergo rehabilitation therapy to improve their mobility. Electromyography (EMG) can be used to improve the effectiveness of at-home therapy programs, as it can assess recovery progress in the absence of a health professional. In particular, EMG armbands have the advantage of being easy to use compared to other EMG technologies, which could allow patients to complete therapy programs without external assistance. However, it is unclear whether there are drawbacks associated with the fixed electrode placement imposed by current armband designs. This study compared the hand gesture prediction capabilities of an off-the-shelf EMG armband with fixed electrode placement and an EMG setup with flexible electrode positioning. Methods: Ten able-bodied participants performed a series of hand and finger gestures with their dominant hand, once with an EMG armband (Untargeted condition) and once with electrodes deliberately placed on specific muscles (Targeted condition). EMG features were extracted from overlapping sliding windows and were used to (1) classify the gestures and (2) predict finger joint positions as measured by a robotic hand exoskeleton. Results: For the classification task, a logistic regression model performed significantly better (p< 0.001) for the Targeted condition (55.8 % ± 10.1 %) compared to the Untargeted condition (47.9 % ± 11.6 %). For the regression task, a k-nearest neighbours model obtained significantly lower (p= 0.007) mean RMSE values for the Targeted condition (0.260 ± 0.037) compared to the Untargeted condition (0.270 ± 0.043). Conclusion: We observed a trade-off between predictive accuracy and ease-of-use of the EMG devices used in this study. It is important to consider such a trade-off when developing clinical applications such as at-home stroke rehabilitation therapy programs.