Integration of multiscale fusion of residual neural network with 2-D gramian angular fields for lower limb movement recognition based on multi-channel sEMG signals

Hao Zhou, Ruliang Feng, Yinghu Peng, Dingxun Jin, Xiaohui Li, Dahua Shou, Guanglin Li, Lin Wang

Abstract: Highlights•A Multi-channel sEMG-driven lower limb movement recognition (LLMR) method was proposed based on Gramian Angular Fields (GAF) and multiscale fusion of Residual Neural Network (MS-ResNet).•Experimental analysis investigated the impact of the convolutional kernel size (k × k) in Stream 2 of MS-ResNet and the number of muscles involved on recognition performance.•The proposed method was compared with those of the related studies in the recognition performance.•This method provides a viable solution for developing more efficient and reliable lower limb movement recognition systems.