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Drivers' Sleepiness Classification using Machine Learning with Physiological and Contextual data

Authors:

Shaibal Barua, Mobyen Uddin Ahmed, Shahina Begum

Research group:

Artificial Intelligence and Intelligent Systems

Publication Type:

Conference/Workshop Paper

Venue:

First International Conference on Advances in Signal Processing and Artificial Intelligence

Abstract

Analysing physiological parameters together with contextual information of car drivers to identify drivers’ sleepiness is a challenging issue. Machine learning algorithms show high potential in data analysis and classification tasks in many domains. This paper presents a use case of machine learning approach for drivers’ sleepiness classification. The classifications are conducted based on drivers’ physiological parameters and contextual information. The sleepiness classification shows receiver operating characteristic (ROC) curves for KNN, SVM and RF were 0.98 on 10-fold cross-validation and 0.93 for leave-one-out (LOO) for all classifiers.

Bibtex

@inproceedings{Barua5365,
author = {Shaibal Barua and Mobyen Uddin Ahmed and Shahina Begum},
title = {Drivers' Sleepiness Classification using Machine Learning with Physiological and Contextual data},
month = {March},
year = {2019},
booktitle = {First International Conference on Advances in Signal Processing and Artificial Intelligence},
url = {http://www.es.mdu.se/publications/5365-}
}