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Faculty of Engineering, University of Porto (FEUP), Rua Dr. Roberto Frias, sn, 4200-465 Porto, Portugal

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1 Papers

Detection of Drowsy Driving Using Wearable Sensors

Topics: Data Mining; Support Vector Machines; Data Science; Feature Selection

Authors: Duarte Pereira ¹ , Brigida Faria ^{2

;

3} and Luis Paulo Reis ^{1

;

3}

Affiliations: ¹ Faculty of Engineering, University of Porto (FEUP), Rua Dr. Roberto Frias, sn, 4200-465 Porto, Portugal , ² ESS, Polytechnic of Porto (ESS-P.PORTO), Rua Dr. Antonio Bernardino de Almeida, 400 4200 - 072 Porto, Portugal , ³ Artificial Intelligence and Computer Science Laboratory (LIACC- Member of LASI LA), Rua Dr. Roberto Frias, sn 4200-465 Porto, Portugal

Digital Object Identifier: 10.5220/0012089900003541

Proceedings of DATA 2023, ISBN: 978-989-758-664-4

Quick Abstract

Drowsy driving is one of the leading causes of traffic accidents. Some solution provides feedback when the driver is drowsy, however, few tackle the issue in a way that allows for portability and early prevision. This study focuses on drowsiness detection during driving. Wearable sensors are used, for a low-cost, portable, automated, and non-intrusive solution. The wearable sensors chosen for biosignal acquisition are Empatica’s E4 wristband for heart activity acquisition and Brainlink Pro for brain activity. Features were mainly in the time domain and time-frequency, and algorithms, such as Nearest Neighbours, Radial Basis Function, Support Vector Machine, Decision Tree, Random Forest, Multi-layer Perceptron, Naive Bayes, and Logistic Regression were trained and validated through the use of a database developed for this study (11 adults with normal last-night sleep, and 2 without any last-night sleep). Participants answered Pittsburgh, and Satisfaction, Alertness, Timing, Efficiency and Duration questionnaires, after which photoplethysmography and electroencephalography physiological signals were acquired during driving in a simulation environment. The practice-run discrimination and individual classification had comparable results, both slightly above average (70 to 80%). The evaluation metric values showed that the discrimination of sleep-deprived exams yielded significantly better. This suggests that the proposed methodology is capable of classifying sleep deprivation and surpasses existing ones in its portability.