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All Journal International Journal of Electrical and Computer Engineering Civil Engineering Journal
Sivaprakasam, Avenaish
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering

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Advancement in driver drowsiness and alcohol detection system using internet of things and machine learning Sivaprakasam, Avenaish; Yogarayan, Sumendra; Mogan, Jashila Nair; Razak, Siti Fatimah Abdul; Abdullah, Mohd. Fikri Azli; Azman, Afizan; Raman, Kavilan
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i3.pp3477-3493

Abstract

Globally traffic accidents are influenced by factors such as drowsiness and alcohol consumption. Consequently, there has been a considerable focus on the development of detection systems as part of ongoing efforts to mitigate these risks. This review paper aims to offer a comprehensive analysis of various drowsiness and alcohol detection methods. The paper particularly emphasizes drowsiness and alcohol detection methods, including those centered on sensor-based approaches, physiological-based techniques, and visual analysis of the eye and mouth state. The aim is to evaluate their method, effectiveness and highlight recent advancements within this domain. Additionally, this review paper evaluates the research gaps of these detection methods, considering factors such as precision, sensitivity, specificity, and adaptability to different environmental conditions.
Driver Drowsiness and Alcohol Detection for Automotive Safety Systems Sivaprakasam, Avenaish; Yogarayan, Sumendra; Mogan, Jashila Nair; Abdul Razak, Siti Fatimah; Azman, Afizan; Raman, Kavilan
Civil Engineering Journal Vol. 11 No. 7 (2025): July
Publisher : Salehan Institute of Higher Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/CEJ-2025-011-07-03

Abstract

Driver drowsiness and alcohol impairment are major causes of traffic accidents, making road safety a main concern. This study highlights the importance of addressing these issues through improved driver monitoring technologies. A prototype combining MQ-3 alcohol sensors, and facial detection was created, integrating with IoT via a Raspberry Pi to monitor and alert on drowsiness and alcohol levels. The developments use the NTHU-DDD dataset, which supports a supervised learning approach to develop a reliable drowsiness detection model. The study explored various machine learning algorithms such as Logistic Regression, Support Vector Machine (SVM), Random Forest (RF), K-nearest neighbors (KNN), Gradient Boosting Classifier, and Gaussian Naive Bayes, with Random Forest and Gradient Boosting emerging as top performers, particularly suited to complex non-linear data. The system effectively used supervised learning techniques to differentiate drowsy and non-drowsy images and exhibited consistent accuracy in detecting drowsiness, especially when the driver’s face was centered. However, accuracy decreased when faces were tilted, highlighting areas for refinement. Moreover, the environmental tests on the MQ-3 sensor demonstrated its sensitivity to alcohol presence, even distinguishing the intensity based on beverage type and concentration. The findings underscore the efficacy of using sensor-based technologies in real-world conditions and provide a foundation for optimizing the system's detection capabilities across various scenarios.
Co-Authors Abdul Razak, Siti Fatimah Abdullah, Mohd. Fikri Azli Azman, Afizan Mogan, Jashila Nair Raman, Kavilan Siti Fatimah Abdul Razak Yogarayan, Sumendra