Evaluating Drivers’ States in Sleepiness Countermeasures Experiments Using Physiological and Eye Data – Hybrid Logistic and Linear Regression Model
Objective sleepiness evaluation is essential for the effect analysis of countermeasures for driver sleepiness, such as in-car stimulants. Furthermore, measuring drivers’ sleepiness in simulator studies also becomes important when investigating causes for task-related sleepiness, for example driving on monotonous routes, which requires little driver engagement. To evaluate driver sleepiness and the effect of countermeasures, we developed a model for predicting sleepiness using both simple logistic and linear regression of heart rate variability, skin conductance and pupil diameter. The algorithm was trained and tested with data from 88 participants in driving simulator studies. A prediction accuracy of 77% was achieved and the model’s sensitivity to thermal stimulation was shown.
How to Cite:
Schmidt, E. & Ochs, J. & Decke, R. & Bullinger, A., (2017) “Evaluating Drivers’ States in Sleepiness Countermeasures Experiments Using Physiological and Eye Data – Hybrid Logistic and Linear Regression Model”, Driving Assessment Conference 9(2017), 284-290. doi: https://doi.org/10.17077/drivingassessment.1648
Rights: Copyright © 2017 the author(s)