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Content Provider | IET Digital Library |
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Author | Silveira, Cláudia Sofia Cardoso, Jaime S. Lourenço, André L. Ahlström, Christer |
Abstract | The first in-depth study on the use of electrocardiogram and electrooculogram for subject-dependent classification in driver sleepiness/fatigue under realistic driving conditions is presented in this work. Since acquisitions in simulated environments may be misleading for sleepiness assessment, performing studies on road are required. For that purpose, the authors present a database resulting from a field driving study performed in the SleepEye project. Based on previous research, supervised machine learning methods are implemented and applied to 16 heart- and 25 eye-based extracted features, mostly related to heart rate variability and blink events, respectively, in order to study the influence of subject dependency in sleepiness classification, using different classifiers and dealing with imbalanced class distributions. Results showed a significantly worse performance in subject-independent classification: a decrease of ∼40 and 20% in the detection rate of the ‘sleepy’ class for two and three classes, respectively. Since physiological signals are the ones that present the most individual characteristics, a subject-independent classification can be even harder to perform. Transfer learning techniques and methods for imbalanced distributions are promising approaches and need further investigation. |
Starting Page | 347 |
Ending Page | 355 |
Page Count | 9 |
ISSN | 1751956X |
Volume Number | 13 |
e-ISSN | 17519578 |
Issue Number | Issue 2, Feb (2019) |
Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/iet-its/13/2 |
Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/10.1049/iet-its.2018.5284 |
Journal | IET Intelligent Transport Systems |
Publisher Date | 2018-10-04 |
Access Restriction | Open |
Rights Holder | © The Institution of Engineering and Technology |
Subject Keyword | 25 Eye-based Extracted Features Bioelectric Signal Biology And Medical Computing Cardiology Detection Rate Digital Signal Processing Driver Sleepiness Detection Driver Sleepiness/fatigue Electrical Activity in Neurophysiological Processes Electro-oculography Electrocardiogram Electrocardiography Electrodiagnostics And Other Electrical Measurement Technique Electrooculogram Eye Feature Extraction Heart Rate Variability Imbalanced Class Distribution Imbalanced Distribution In-depth Study Knowledge Engineering Technique Learning in AI Medical Signal Detection Medical Signal Processing Pattern Classification Realistic Condition Realistic Driving Condition Signal Classification Sleepiness Assessment Sleepiness Classification Statistics Subject Dependency Subject-dependent Classification Subject-independent Classification Supervised Machine Learning Method Worse Performance |
Content Type | Text |
Resource Type | Article |
Subject | Law Transportation Environmental Science Mechanical Engineering |
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