基于深度收缩稀疏自编码网络的飞行员疲劳状态识别

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

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(0.5～4 Hz)、theta波(5～8 Hz)、alpha波(7～14 Hz)和beta波(14～30 Hz),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 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(0.5 ~ 4 Hz), theta wave(5 ~ 8 Hz), alpha wave(7 ~ 14 Hz)and beta wave(14 ~ 30 Hz) 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.