This code was used for my thesis Predicting (drops in) vigilance using Machine Learning. Others can use these scripts as long as they reference to my original thesis.
Abstract
The prediction of (drops in) vigilance can be useful in the prevention of car crashes. Previous work showed that temperature data, demographic data and subjective measurements can be useful in these predictions. With machine learning this study explores these different features as possible predictors of vigilance in the Brief Stimulus Reaction Time task (BSRT). Three different kinds of Random Forest models were trained, and a naïve model was created for the evaluation of these models. Temperature was measured four seconds prior to the target presentation with iButtons and a FLIR camera. Results show that features measured with the FLIR camera may be useful predictors of vigilance, especially the forehead temperature. However, the accuracy scores of the models were just slightly better than the naïve model. So, further research should explore these features further and confirm these findings.
APA Reference
Lucas, R. E. (2021). Predicting (drops in) Vigilance using Machine Learning (Unpublished
Bachelor’s Thesis). Utrecht University.