基于深度收缩稀疏自编码网络的飞行员疲劳状态识别

飞行员的疲劳状态识别具有重要的研究意义和应用价值.针对飞行员疲劳状态识别的复杂性和准确性,提出一种新的基于脑电信号的飞行员疲劳状态识别深度学习模型.在对飞行员的脑电信号进行滤波分解的基础上,提取delta波(0.5sim4Hz)、theta波(5sim8Hz)、alpha波(7sim14Hz)和beta波(14sim30Hz),将其重组信号作为深度收缩稀疏自编码网络-Softmax模型的输入向量,用以对飞行员疲劳状态的识别,所得到的实验结果与深度自编码网络-Softmax模型和传统方法PCA-Softmax模型识别结果进行比较,结果表明所建立的深度学习模型具有很好的分类效果,分类准确率可达91.67%,且学习所得的特征稳定性好,验证了所提模型具有稳定性和重复验证性.

Recognition of fatigue status of pilots based on deep contractive sparse auto-encoding network

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. The delta wave (0.5sim4Hz), theta wave (5sim8Hz), alpha wave (7sim14Hz) and beta wave (14sim30Hz) are extracted by multi-scale decomposition of electroencephalogram signals using filters, and the reconstruction signals of them are taken as the input vectors of the model. A deep contractive sparse auto-encoding network-Softmax model is proposed for identifying pilots'' fatigue status, and its recognition results are also compared with these of the deep auto-encoding network-Softmax and traditional PCA-Softmax model. The results show that the proposed deep learning model not only has a nice classification, the accuracy of which is up to 91.67%, but also the learned features are stable, and the proposed model is stable and reusable verified.