Recognition of Fatigue Status of Pilots Based on Deep Contractive Sparse Auto-Encoding Network

摘要

Recognition of fatigue status of pilots has important research significance. Aiming at the complexity and accuracy of recognition of fatigue status of pilots, a new deep learning model based on electroencephalogram signals is proposed to recognize fatigue status of pilots. Firstly, the delta wave (0.5~4Hz), theta wave (5~8Hz), alpha wave (7~14Hz) and beta wave (14~30Hz) are extracted by multi-scale decomposition of electroencephalogram signals using filters, and the reconstruction of them are input vectors of the model. Secondly, a deep contractive sparse auto-encoding network-Softmax model is proposed for identifying pilots' fatigue status and its recognition results are also compared with those of the deep auto-encoding network-Softmax and traditional PCA-Softmax model. Lastly, the results show that the proposed deep learning model not only has a nice classification, whose accuracy rate is up to 91.17%, but also the learned features is stable, and the proposed model is stable and reusable verified.