Real-time drowsiness detection using wearable, lightweight brain sensing headbands
The feasibility of real-time drowsiness detection using commercially available, off-the-shelf, lightweight, wearable electroencephalogram (EEG) sensors is explored. While EEG signals are known to be reliable indicators of fatigue and drowsiness, they have not been used widely due to their size and form factor. However, the use of lightweight wearable EEGs alleviates this concern. Spectral analysis of EEG signals from these sensors using support vector machines (SVMs) is shown to classify drowsy states with high accuracy. The system is validated using data collected on 23 subjects in fresh and drowsy states. An accuracy of 81% is obtained at a per-subject level and 74% in cross-subject validation using SVM with radial basis kernel. Using a temporal aggregation strategy, the cross-subject validation accuracy is shown to improve to 87%. The EEG signals are also used to characterise the blink duration and frequency of subjects. However, classification of drowsy states using blink analysis is shown to have lower accuracy than that using spectral analysis.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/1751956X
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Supplemental Notes:
- Abstract reprinted with permission of the Institution of Engineering and Technology.
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Authors:
- Rohit, Fnu
- Kulathumani, Vinod
- Kavi, Rahul
- Elwarfalli, Ibrahim
- Kecojevic, Vlad
- Nimbarte, Ashish
- Publication Date: 2017-6
Language
- English
Media Info
- Media Type: Web
- Features: Figures; References; Tables;
- Pagination: pp 255-263
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Serial:
- IET Intelligent Transport Systems
- Volume: 11
- Issue Number: 5
- Publisher: Institution of Engineering and Technology (IET)
- ISSN: 1751-956X
- EISSN: 1751-9578
- Serial URL: https://ietresearch.onlinelibrary.wiley.com/journal/17519578
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Publication flags:
Open Access (libre)
Subject/Index Terms
- TRT Terms: Accuracy; Brain; Detection and identification technologies; Drowsiness; Intelligent transportation systems; Sensors; Spectrum analysis; Validation
- Subject Areas: Highways; Operations and Traffic Management; Safety and Human Factors;
Filing Info
- Accession Number: 01640974
- Record Type: Publication
- Files: TRIS
- Created Date: Jul 14 2017 11:08AM