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The recognition of lower limb movement has a wide range of applications in rehabilitation training, wearable exoskeleton control, and human activity monitoring. Surface electromyography (sEMG) signals can directly reflect the intention of human movement and can be used as the source of lower limb movement recognition. Literature reports have shown that extracting features from sEMG signals is the core of human movement recognition based on sEMG signals. However, how to effectively extract features from the sEMG signal of the lower limbs affected by body gravity is a difficult problem for the recognition of lower limb movement based on the sEMG signal.

The main objective of this paper is to propose an efficient lower limb movement recognition model based on sEMG signals to accurately recognize the four lower limb movements.

We proposed a novel method of …