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Content Provider | IEEE Xplore Digital Library |
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Author | Tran, Y. Craig, A. Wijesuriya, N. Hung Nguyen |
Copyright Year | 2010 |
Description | Author affiliation: Key University Research Centre in Health Technologies, Faculty of Engineering and Information Technology, University of Technology, Sydney, Australia (Tran, Y.) || Rehabilitation Studies Unit, University of Sydney, Australia (Craig, A.; Wijesuriya, N.) || Faculty of Engineering and Information Technology, University of Technology, Sydney, Australia (Hung Nguyen) |
Abstract | Fatigue can be defined as a state that involves psychological and physical tiredness with a range of symptoms such as tired eyes, yawning and increased blink rate. It has major implications for work place and road safety as well as a negative symptom of many acute and chronic illnesses. As such there has been considerable research dedicated to systems or algorithms that can be used to detect and monitor the onset of fatigue. This paper examines using electroencephalography (EEG) signals to classify fatigue and alert states as a function of subjective self-report, driving performance and physiological symptoms. The results show that EEG classification network for fatigue improved from 75% to 80% when these factors are applied, especially when the data is grouped by subjective self-report of fatigue with classification accuracy improving to 84.5%. |
Starting Page | 4460 |
Ending Page | 4463 |
File Size | 380366 |
Page Count | 4 |
File Format | |
ISBN | 9781424441235 |
ISSN | 1557170X |
DOI | 10.1109/IEMBS.2010.5625964 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2010-08-31 |
Publisher Place | Argentina |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Fatigue Electroencephalography Biomedical monitoring Driver circuits Psychology Brain modeling Artificial neural networks |
Content Type | Text |
Resource Type | Article |
Subject | Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition |
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