Conference Proceeding

Evaluating Drivers’ States in Sleepiness Countermeasures Experiments Using Physiological and Eye Data – Hybrid Logistic and Linear Regression Model

Authors
  • Elisabeth Schmidt (BMW AG, Munich, Germany)
  • Judith Ochs (BMW AG, Munich, Germany)
  • Ralf Decke (BMW AG, Munich, Germany)
  • Angelika C Bullinger (Technical University Chemnitz, Chemnitz, Germany)

Abstract

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)

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Published on
29 Jun 2017
Peer Reviewed