Driver's drowsiness estimation by combining EEG signal analysis and ICA-based fuzzy neural networks

Chin Teng Lin, Sheng-Fu Liang, Yu Chieh Chen, Yung Chi Hsu, Li Wei Ko

研究成果: Conference contribution

6 引文 斯高帕斯(Scopus)

摘要

The public security has become an important issue in recent years, especially, the safe manipulation and control of vehicles in preventing the growing number of traffic accident fatalities. Accidents caused by drivers' drowsiness have a high fatality rate due to the decline of drivers' abilities in perception, recognition, and vehicle control abilities while sleepy. Preventing such an accident requires a technique for detecting, estimating, and predicting the level of alertness of a driver and a mechanism to maintain the driver's maximum performance of driving. The ICAFNN is a fuzzy neural network (FNN) capable of parameter self-adapting and structure selfconstructing to acquire a small number of fuzzy rules for interpreting the embedded knowledge of a system from the given training data set. Our experiments show that the ICAFNN can achieve significant improvements in the accuracy of drowsiness estimation compared with our previous works.

原文English
主出版物標題ISCAS 2006
主出版物子標題2006 IEEE International Symposium on Circuits and Systems, Proceedings
頁面2125-2128
頁數4
出版狀態Published - 2006
事件ISCAS 2006: 2006 IEEE International Symposium on Circuits and Systems - Kos, Greece
持續時間: 2006 五月 212006 五月 24

Other

OtherISCAS 2006: 2006 IEEE International Symposium on Circuits and Systems
國家Greece
城市Kos
期間06-05-2106-05-24

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Electrical and Electronic Engineering

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  • 引用此

    Lin, C. T., Liang, S-F., Chen, Y. C., Hsu, Y. C., & Ko, L. W. (2006). Driver's drowsiness estimation by combining EEG signal analysis and ICA-based fuzzy neural networks. 於 ISCAS 2006: 2006 IEEE International Symposium on Circuits and Systems, Proceedings (頁 2125-2128). [1693037]