Muscle Connectivity Analysis for Hand Gesture Recognition via sEMG.

Physiological measurement like surface electromyography (sEMG) allows a deeper insight on interactions among subsystems during the human motion coordination. In this paper, we aim to investigate such interactions via functional muscle networks during hand movements, especially when different hand gestures are performed. It is achieved by considering muscle connectivities using Granger Prediction of paired sEMG signals, which were recorded from extrinsic muscles of the upper limb, while participants were sitting upright and performing hand gestures. It is found that by using muscle connectivities obtained by applying the method of Granger Prediction as features, although individual differences exist among subjects, significant connections between pairs of muscles were observed through permutation tests at a group level. Graph theory based on the overall statistical result was used to visualise functional networks by considering all the significant connections which were not bidirectional. We found two distinct networks can be used to represent the differences between two hand gestures. Such insight of functional networks can be a potential candidate to interpret the relationships between muscle pairs, which is helpful for decoding hand gestures.