Recognition of fatigue status of pilots based on deep contractive auto-encoding network_Journal of Biomedical Engineering

Authors：

 HAN Shuang ¹ , WU Qi ¹ , SUN Libing ² , QIU Xuyi ³ , REN He ⁴ , LU Zhao ⁵

1. Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, P.R.China;
2. Shanghai Securtech Information Technology Co., ltd. Shanghai 200240, P.R.China;
3. Chinese Aeronautical Radio Electronics Research Institute, Shanghai 200240, P.R.China;
4. Shanghai Engineering Research Center of Civil Aircraft Health Monitoring, Shanghai 200240, P.R.China;
5. Department of Electrical Engineering, Tuskegee University, Tuskegee, AL 36088, USA;

Corresponding author：

HAN Shuang, Email: han1201@sjtu.edu.cn

Keywords：

pilots’ fatigue; electroencephalogram signals; deep contractive auto-encoding network; wavelet packet transform

DOI：

10.7507/1001-5515.201701018

Video：

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Abstract Full text Figures/Tables Video References Cited by

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.

Citation： HAN Shuang, WU Qi, SUN Libing, QIU Xuyi, REN He, LU Zhao. Recognition of fatigue status of pilots based on deep contractive auto-encoding network. Journal of Biomedical Engineering, 2018, 35(3): 443-451. doi: 10.7507/1001-5515.201701018 Copy

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2.	田利军, 陈甜甜, 王景博. 内部控制、安全文化与航空安全. 中国安全科学学报, 2016, 26(8): 1-6.
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4.	孟豫, 王泉川. 飞行疲劳风险管理体系研究进展. 中国安全科学学报, 2014, 24(11): 10-16.
5.	Reis C, Mestre C, Canhão H. Prevalence of fatigue in a group of airline pilots. Aviat Space Environ Med, 2013, 84(8): 828-833.
6.	刘俊杰, 张丽娟. 飞行疲劳事件语义网络分析. 中国安全科学学报, 2016, 26(1): 34-39.
7.	Borghini G, Astolfi L, Vecchiato G, et al. Measuring neurophysiological signals in aircraft pilots and car drivers for the assessment of mental workload, fatigue and drowsiness. Neurosci Biobehav Rev, 2014, 44: 58-75.
8.	Ghaemi A, Rashedi E, Pourrahimi A M, et al. Automatic channel selection in EEG signals for classification of left or right hand movement in Brain Computer Interfaces using improved binary gravitation search algorithm. Biomed Signal Process Control, 2017, 33: 109-118.
9.	Chen Chunxiao, Wang Jing, Li Kun, et al. Assessment visual fatigue of watching 3DTV using EEG power spectral parameters. Displays, 2014, 35(5): 266-272.
10.	Slanzi G, Balazs J A, Velasquez J D. Combining eye tracking, pupil dilation and EEG analysis for predicting web users click intention. Information Fusion, 2017, 35: 51-57.
11.	李明爱, 马建勇, 杨金福. 基于小波包和熵准则的最优频段提取方法. 仪器仪表学报, 2012, 33(8): 1721-1728.
12.	Rifai S, Vincent P, Muller X, et al. Contractive auto-encoders: explicit invariance during feature extraction// Proceedings of the Twenty-eight International Conference on Machine Learning. Bellevue: IMLS, 2011: 833-840.
13.	Stober S, Cameron D J, Grahn J A. Classifying EEG recordings of rhythm perception// International Society for Music Information Retrieval Conference. Taipei: ISMIR, 2014: 649-654.
14.	Chai Xin, Wang Qisong, Zhao Yongping, et al. Unsupervised domain adaptation techniques based on auto-encoder for non-stationary EEG-based emotion recognition. Comput Biol Med, 2016, 79: 205-214.
15.	Patriche C V, Pirnau R, Grozavu A, et al. A comparative analysis of binary logistic regression and analytical hierarchy process for landslide susceptibility assessment in the Dobrovăţ River Basin, Romania. Pedosphere, 2016, 26(3): 335-350.
16.	葛哲学, 沙威. 小波分析理论与MATLAB R2007实现. 北京: 电子工业出版社, 2007.
17.	Chai R, Naik G R, Tran Y, et al. Classification of driver fatigue in an electroencephalography-based countermeasure system with source separation module// 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Milan, Italy, 2015: 514-517.

1. Caldwell J A, Mallis M M, Caldwell J L, et al. Fatigue countermeasures in aviation. Aviat Space Environ Med, 2009, 80(1): 29-59.
2. 田利军, 陈甜甜, 王景博. 内部控制、安全文化与航空安全. 中国安全科学学报, 2016, 26(8): 1-6.
3. 张正勋. HFACS 在民航训练飞行人为差错分析中的应用. 成都: 电子科技大学, 2010.
4. 孟豫, 王泉川. 飞行疲劳风险管理体系研究进展. 中国安全科学学报, 2014, 24(11): 10-16.
5. Reis C, Mestre C, Canhão H. Prevalence of fatigue in a group of airline pilots. Aviat Space Environ Med, 2013, 84(8): 828-833.
6. 刘俊杰, 张丽娟. 飞行疲劳事件语义网络分析. 中国安全科学学报, 2016, 26(1): 34-39.
7. Borghini G, Astolfi L, Vecchiato G, et al. Measuring neurophysiological signals in aircraft pilots and car drivers for the assessment of mental workload, fatigue and drowsiness. Neurosci Biobehav Rev, 2014, 44: 58-75.
8. Ghaemi A, Rashedi E, Pourrahimi A M, et al. Automatic channel selection in EEG signals for classification of left or right hand movement in Brain Computer Interfaces using improved binary gravitation search algorithm. Biomed Signal Process Control, 2017, 33: 109-118.
9. Chen Chunxiao, Wang Jing, Li Kun, et al. Assessment visual fatigue of watching 3DTV using EEG power spectral parameters. Displays, 2014, 35(5): 266-272.
10. Slanzi G, Balazs J A, Velasquez J D. Combining eye tracking, pupil dilation and EEG analysis for predicting web users click intention. Information Fusion, 2017, 35: 51-57.
11. 李明爱, 马建勇, 杨金福. 基于小波包和熵准则的最优频段提取方法. 仪器仪表学报, 2012, 33(8): 1721-1728.
12. Rifai S, Vincent P, Muller X, et al. Contractive auto-encoders: explicit invariance during feature extraction// Proceedings of the Twenty-eight International Conference on Machine Learning. Bellevue: IMLS, 2011: 833-840.
13. Stober S, Cameron D J, Grahn J A. Classifying EEG recordings of rhythm perception// International Society for Music Information Retrieval Conference. Taipei: ISMIR, 2014: 649-654.
14. Chai Xin, Wang Qisong, Zhao Yongping, et al. Unsupervised domain adaptation techniques based on auto-encoder for non-stationary EEG-based emotion recognition. Comput Biol Med, 2016, 79: 205-214.
15. Patriche C V, Pirnau R, Grozavu A, et al. A comparative analysis of binary logistic regression and analytical hierarchy process for landslide susceptibility assessment in the Dobrovăţ River Basin, Romania. Pedosphere, 2016, 26(3): 335-350.
16. 葛哲学, 沙威. 小波分析理论与MATLAB R2007实现. 北京: 电子工业出版社, 2007.
17. Chai R, Naik G R, Tran Y, et al. Classification of driver fatigue in an electroencephalography-based countermeasure system with source separation module// 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Milan, Italy, 2015: 514-517.

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