An Electrode Configuration Framework for sEMG-Based Gesture Recognition With Muscle-Matched Layouts

Gesture recognition via the measurement of surface electromyography (sEMG) has been extensively applied in human–machine interface (HMI) scenarios. Although high-density (HD) electrodes are effective for gesture recognition, channel selection from HD electrodes contributes to computational efficiency and wearable feasibility in practical applications. Nonetheless, the majority of the existing methods prioritize classification accuracy (CA) over operational effectiveness. During the framework implementation, channel forms, the optimization-oriented number of gestures, and the number of trials for optimization and training (
$\text{O}\&\text{T}$
) are related to the optimized layout and the duration of frameworks. To improve the operational effectiveness, we presented an electrode configuration method to simplify the framework process. The muscle-matched electrode configuration (MEC) method was evaluated on 11 subjects with 11-gesture recognition, and compared with two traditional channel selection methods, i.e., uniform configuration (UNI) and sequential forward searching (SFS) configuration. The experimental results showed that the bipolar channel outperformed the monopolar channel in terms of CA. Further results demonstrated the superiority of MEC in operational effectiveness by reducing the optimization-oriented number of gestures and the
$\text{O}\&\text{T}$
-oriented number of trials. Specifically in the six-channel configuration, SFS required nine-trial and five-gesture optimization to accomplish a comparable CA (
$90.25\% \pm 3.49\%$
), whereas MEC only took one trial with the same number of gestures optimization to achieve reliable performance (
$92.61\% \pm 5.18\%$
), which saved nearly 85% of the training time and memory space. Overall, the outcomes of this study provide meaningful guidelines about the efficient configuration framework for sEMG-based gesture recognition in practical applications.