Introduction: Human–Machine Interaction is a research area that has been gaining increasing attention due to the search for new, more natural, and intuitive interfaces based on the use of new technologies which facilitate the interaction of users with smart devices. In this context, there have been attempts to develop gesture-based interfaces. However, a fundamental question still needs to be addressed centred around whether the gestures used are indeed intuitive. To this end, questionnaires answered by volunteers are usually used, but this type of response is quite subjective. The use of neurophysiological indicators may be an alternative to finding an objective and efficient metric of intuitiveness. This study aimed to test the hypothesis that the improved coordination of muscle synergies serves as a reliable indicator of gesture intuitiveness.

Methods: EMG signals from 16 muscles were monitored, and muscle networks were constructed from the extraction of muscle synergies obtained using Non-Negative Matrix Factorization (NMF) and also from Intermuscular Coherence (IMC). The muscle networks obtained using both approaches in four frequency bands were analyzed in their spatial structure and also using metrics (such as Weighted Global Efficiency (WGE) and Effective Average Strength (EAS)). The correlation of these metrics with the Intuitiveness Level (IL) associated with each gesture was then calculated.

Results and Discussion: The networks from muscle synergies show denser connectivity levels than IMC. Notably, WGE values of synergy muscle networks in the Beta and Gamma2 bands, as well as EAS values of IMC muscle networks in the Gamma1 band, positively correlate with IL values.

Conclusions: The results provide substantial evidence supporting a significant correlation between the intuitiveness level and muscle synergies analyzed using both NMF and IMC approaches.