EOG-based Drowsiness Detection Using Convolutional Neural Networks

被引:0
作者
Zhu, Xuemin [1 ]
Zheng, Wei-Long [1 ]
Lu, Bao-Liang [1 ]
Chen, Xiaoping [2 ]
Chen, Shanguang [2 ]
Wang, Chunhui [2 ]
机构
[1] Shanghai Jiao Tong Univ, Dept Comp Sci & Engn, Key Lab Shanghai Educ Commiss, Shanghai 200030, Peoples R China
[2] China Astronaut Res & Training Ctr, Natl Key Lab Human Factors Engn, Beijing, Peoples R China
来源
PROCEEDINGS OF THE 2014 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) | 2014年
关键词
SLOW EYE-MOVEMENTS; VIGILANCE ESTIMATION;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This study provides a new application of convolutional neural networks for drowsiness detection based on electrooculography (EOG) signals. Drowsiness is charged to be one of the major causes of traffic accidents. Such application is helpful to reduce losses of casualty and property. Most attempts at drowsiness detection based on EOG involve a feature extraction step, which is accounted as time-consuming task, and it is difficult to extract effective features. In this paper, an unsupervised learning is proposed to estimate driver fatigue based on EOG. A convolutional neural network with a linear regression layer is applied to EOG signals in order to avoid using of manual features. With a postprocessing step of linear dynamic system (LDS), we are able to capture the physiological status shifting. The performance of the proposed model is evaluated by the correlation coefficients between the final outputs and the local error rates of the subjects. Compared with the results of a manual ad-hoc feature extraction approach, our method is proven to be effective for drowsiness detection.
引用
收藏
页码:128 / 134
页数:7
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