Fatigue recognition of operators in a complex human-machine interface can effectively avoid human error. Aiming at improving the operator's vigilance in the digital control room of nuclear power plants, this paper proposes a multimodal fatigue state recognition technique. It combines deep learning and eyelid feature information to recognize the fatigue state of operators. Temporal Convolutional Network (TCN) is integrated with the 3D Residual Network (ResNet3D) to retain the temporal and spatial features of the eyelid. By using a custom cosine annealing learning rate decay algorithm to avoid t...
