A Study of Integrated Driver fatigue Judging Algorithm Based on Principal Component Analysis

Zhipeng Ma; Shuwan Yao; Junxian Li

doi:10.1051/matecconf/201817302011

All issues

Volume 173 (2018)

MATEC Web Conf., 173 (2018) 02011

References

Open Access

Issue		MATEC Web Conf. Volume 173, 2018 2018 International Conference on Smart Materials, Intelligent Manufacturing and Automation (SMIMA 2018)


Article Number		02011
Number of page(s)		4
Section		Automation and Nontraditional Manufacturing
DOI		https://doi.org/10.1051/matecconf/201817302011
Published online		19 June 2018

Rongrong Fu, Hong Wang, Wenbo Zhao. Dynamic driver fatigue detection using hidden Markov model in real driving condition. Expert Systems with Applications, 63 (2016) 397–411. [CrossRef] [Google Scholar]
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T.P. jung, makeig S, sensmo M, etal. Estimating Alertness from the EEG Power Spectrum. IEEE Transactions on Biomedical Engineering, 1997, 44(1):60–69. [CrossRef] [Google Scholar]
boveris S. Driver fatigue monitoring technologies and future ideas, Proceedings of AWAKE Road Safety Workshop. Balocco, Italy: Elsevier Science Ltd, 2004. [Google Scholar]
S. Miloevic. Driver’s fatigue studies. Economics, 1997, 40(3):381-38. [Google Scholar]
He Qiwen. Research on driver fatigue detection based on multi-feature integration. Changsha: Central South University of Forestry and Technology, 2009. [Google Scholar]
G.F.Wilson, R.D.Donnell. Measurement of operator workload with the neuropsychological workload test battery. Advances in Psychology, 1988, (52):63-100. [CrossRef] [Google Scholar]
lin C, K0 L, chung I, et al. Adaptive EEG-Based Alertness Estimation System by Using ICA-Based Fuzzy Neural Networks. IEEE Transactions on Circuits and Systems-I, 2006, 53(11):2469-2476. [CrossRef] [Google Scholar]
Wang Ning. Study of fatigue state recognition based on multiple information fusion. Jinan: Shandong University, 2016. [Google Scholar]
Mengzhu Guo, Shiwu Li, Linhong Wang, Meng Chai, Facheng Chen and Yunong Wei. Research on the Relationship between Reaction Ability and Mental State for Online Assessment of Driving Fatigue. Public Health, 2016, 13, 1174. [Google Scholar]
Du Yunxiao, Wang Shuyi, Ndaro Nyakuru Zaphlene, ZUO Yan. Comparative study on evaluation algorithms for neck muscle fatigue based on surface electromyography signal. Journal of Biomedical Engineering. 2018, Vol. 35, No. 1: 31-37 [Google Scholar]

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[1] Rongrong Fu, Hong Wang, Wenbo Zhao. Dynamic driver fatigue detection using hidden Markov model in real driving condition. Expert Systems with Applications, 63 (2016) 397–411. [CrossRef] [Google Scholar]

[2] dinges D F, mallis M, maisling, etal. Evaluation of techniques for ocular measurement as an index of fatigue and the basis for alertness management, National Highway Traffic Safety Administration, USA, 1998. [Google Scholar]

[3] T.P. jung, makeig S, sensmo M, etal. Estimating Alertness from the EEG Power Spectrum. IEEE Transactions on Biomedical Engineering, 1997, 44(1):60–69. [CrossRef] [Google Scholar]

[4] boveris S. Driver fatigue monitoring technologies and future ideas, Proceedings of AWAKE Road Safety Workshop. Balocco, Italy: Elsevier Science Ltd, 2004. [Google Scholar]

[5] S. Miloevic. Driver’s fatigue studies. Economics, 1997, 40(3):381-38. [Google Scholar]

[6] He Qiwen. Research on driver fatigue detection based on multi-feature integration. Changsha: Central South University of Forestry and Technology, 2009. [Google Scholar]

[7] G.F.Wilson, R.D.Donnell. Measurement of operator workload with the neuropsychological workload test battery. Advances in Psychology, 1988, (52):63-100. [CrossRef] [Google Scholar]

[8] lin C, K0 L, chung I, et al. Adaptive EEG-Based Alertness Estimation System by Using ICA-Based Fuzzy Neural Networks. IEEE Transactions on Circuits and Systems-I, 2006, 53(11):2469-2476. [CrossRef] [Google Scholar]

[9] Wang Ning. Study of fatigue state recognition based on multiple information fusion. Jinan: Shandong University, 2016. [Google Scholar]

[10] Mengzhu Guo, Shiwu Li, Linhong Wang, Meng Chai, Facheng Chen and Yunong Wei. Research on the Relationship between Reaction Ability and Mental State for Online Assessment of Driving Fatigue. Public Health, 2016, 13, 1174. [Google Scholar]

[11] Du Yunxiao, Wang Shuyi, Ndaro Nyakuru Zaphlene, ZUO Yan. Comparative study on evaluation algorithms for neck muscle fatigue based on surface electromyography signal. Journal of Biomedical Engineering. 2018, Vol. 35, No. 1: 31-37 [Google Scholar]