Recognition of fatigue status of pilots based on deep contractive auto-encoding network / 生物医学工程学杂志
Journal of Biomedical Engineering
;
(6): 443-451, 2018.
Article
in Chinese
| WPRIM
| ID: wpr-687610
ABSTRACT
We proposed a new deep learning model by analyzing electroencephalogram signals to reduce the complexity of feature extraction and improve the accuracy of recognition of fatigue status of pilots. For one thing, we applied wavelet packet transform to decompose electroencephalogram signals of pilots to extract the δ wave (0.4-3 Hz), θ wave (4-7 Hz), α wave (8-13 Hz) and β wave (14-30 Hz), and the combination of them was used as de-nosing electroencephalogram signals. For another, we proposed a deep contractive auto-encoding network-Softmax model for identifying pilots' fatigue status. Its recognition results were also compared with other models. The experimental results showed that the proposed deep learning model had a nice recognition, and the accuracy of recognition was up to 91.67%. Therefore, recognition of fatigue status of pilots based on deep contractive auto-encoding network is of great significance.
Full text:
Available
Index:
WPRIM (Western Pacific)
Language:
Chinese
Journal:
Journal of Biomedical Engineering
Year:
2018
Type:
Article
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