Validation and Interpretation of a Multimodal Drowsiness Detection System Using Explainable Machine Learning

Article


Hasan, Md Mahmudul, Watling, Christopher N. and Larue, Grégoire S.. 2024. "Validation and Interpretation of a Multimodal Drowsiness Detection System Using Explainable Machine Learning." Computer Methods and Programs in Biomedicine. 243. https://doi.org/10.1016/j.cmpb.2023.107925
Article Title

Validation and Interpretation of a Multimodal Drowsiness Detection System Using Explainable Machine Learning

ERA Journal ID5039
Article CategoryArticle
AuthorsHasan, Md Mahmudul, Watling, Christopher N. and Larue, Grégoire S.
Journal TitleComputer Methods and Programs in Biomedicine
Journal Citation243
Article Number107925
Number of Pages16
Year2024
PublisherElsevier
Place of PublicationIreland
ISSN0169-2607
1872-7565
Digital Object Identifier (DOI)https://doi.org/10.1016/j.cmpb.2023.107925
Web Address (URL)https://www.sciencedirect.com/science/article/pii/S0169260723005916
Abstract

Background and Objective: Drowsiness behind the wheel is a major road safety issue with efforts focused on developing drowsy driving detection systems. However, most drowsy driving detection studies using physiological signals have focused on developing a 'black box' machine learning classifier, with much less focus on 'robustness' and 'explainability'—two crucial properties of a trustworthy machine learning model. Therefore, this study has focused on using multiple validation techniques to evaluate the overall performance of such a system using multiple supervised machine learning-based classifier and then unbox the black box model using explainable machine learning.

Methods: Driving was simulated via a 30-minute psychomotor vigilance task while the participants reported their level of subjective sleepiness with their physiological signals being recorded. Six different techniques, comprising subject-dependent and independent techniques were applied for model validation and robustness testing with three supervised machine learning classifiers (K-nearest neighbours, Support vector machines and Random Forest), and two explainable methods, namely SHapley Additive exPlanation (SHAP) analysis and partial dependency analysis (PDA) were leveraged for model interpretation.

Results: The study identified the leave one participant out, a subject-independent validation technique to be most useful, with the best sensitivity of 70.3%, specificity of 82.2%, and an accuracy of 80.1% using the random forest classifier in addressing the autocorrelation issue due to inter-individual differences in physiological signals. Moreover, the explainable results suggest most important physiological features for drowsiness detection, with a clear cut-off in the decision boundary.

Conclusions: The implication of the study will ensure a rigorous validation for robustness testing and an explainable machine learning approach to developing a trustworthy drowsiness detection system and enhancing road safety. The explainable machine learning-based results show promise in real-life deployment of the physiological-signal based in-vehicle trustworthy drowsiness detection system, with higher reliability and explainability, along with a lower system cost.

Keywordsfeatures, physiological signals, validation, interpretability, SHAP analysis, partial dependency analysis
Article Publishing Charge (APC) FundingOther
ANZSRC Field of Research 2020461199. Machine learning not elsewhere classified
520206. Psychophysiology
520402. Decision making
Byline AffiliationsUniversity of New South Wales
Queensland University of Technology
School of Psychology and Wellbeing
Centre for Health Research
University of the Sunshine Coast
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