The importance of subject-dependent classification and imbalanced class distributions in driver sleepiness detection in realistic conditions

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.

Author(s)
Silviera C. S., Cardoso J. S., Lourenco A. R., Ahlström C.
Research area
Publication type
Published in
IET Intelligent Transport Systems
Project
Year of publication
2018

Safer – Vehicle and Traffic Safety Centre

SAFER is the open innovation arena where researchers and expertise work together to create safe mobility. Our traffic safety approach covers people, vehicles and the infrastructure – and together we contribute to safer road transports and smarter, more sustainable cities.

Contact information

SAFER

Lindholmspiren 3A
SE-417 56 Göteborg
Sweden

 +46 31-772 21 06
 safer [at] chalmers.se