An Integrated Framework for Driver Drowsiness Detection and Alcohol Intoxication using Machine Learning

Varghese, Renju Rachel; Jacob, Pramod Mathew; Jacob, Joanna; Babu, Merlin Nissi; Ravikanth, R; George, S.

doi:10.1109/icdabi53623.2021.9655979

Cited by 15 publications

(4 citation statements)

References 7 publications

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“…Subsequently, the table considers six distinct alcohol detection systems for the in-vehicle ecosystem developed during the last five years (from 2016 to 2021) alongside our proposed in-vehicle alcohol detection system which relies on the optimizable shallow neural networks (O-SNN) as the core learning model. The reported detection schemes incorporate the following supervised learning models: genetic algorithm with support vector machine/radial which has been used by [35], Ross-Quinlan decision trees known as (C4.5 DT), used in the development of alcohol detection system in [50], reduced error pruning tree (REPT-DT) decision tree, which has been employed in [36], the random forest classifier (RFC) model used in [37], support vector machine (SVM) utilized by author of [38], and finally, the k-nearest neighbors (kNN) learning model that is used in [39].…”

Section: Results and Analysismentioning

confidence: 99%

“…The authors adopted a general approach and did not specifically concentrate on the alcohol detection problem. The authors of the paper [38] proposed a low-cost, non-intrusive real-time driver drowsiness detection system that was coupled with an alcohol detection system. The MQ-3 Sensor is used to detect alcohol in this system.…”

Section: Ref Year Detection Systemmentioning

confidence: 99%

“…Year Learning Scheme Accuracy F-Score Kappa [35] 2021 GA+ SVM 97.60% 97.5% 97.90% [50] 2019 C4.5 DT 92.53% -- [36] 2018 REPT DT 87.70% 85.90% - [37] 2019 RFC 97.53% -- [38] 2021 SVM 86.00% 98.00% - [39] 2016 kNN 92.00% 87.50% -Proposed 2022 O-SNN 99.80% 99.70% 99.50%…”

Section: Refmentioning

confidence: 99%

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A Lightweight In-Vehicle Alcohol Detection using Smart Sensing and Supervised Learning

Al‐Haija¹,

Krichen²

2022

Preprint

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According to the risk investigations of being involved in an accident, alcohol-impaired driving is one of the major causes of motor-vehicles accidents. Preventing highly intoxicated persons from driving would potentially save many lives. This paper proposes a lightweight in-vehicle alcohol detection that processes the data generated from 6-alcohol sensors (MQ-3 Alcohol Sensors) using an optimizable shallow neural network (O-SNN). The experimental evaluation results exhibit a high-performance detection system scoring a 99.8% of detection accuracy with a very short inferencing delay of 2.22 µ seconds. Hence, the proposed model can be efficiently deployed and used to discover in-vehicle alcohol with high accuracy and low inference overhead as a part of the driver alcohol detection system for safety (DADSS) system aiming at massive deployment of alcohol sensing systems that could potentially save thousands of lives annually.

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Section: Results and Analysismentioning

confidence: 99%

Section: Ref Year Detection Systemmentioning

confidence: 99%

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A Lightweight In-Vehicle Alcohol Detection using Smart Sensing and Supervised Learning

Al‐Haija¹,

Krichen²

2022

Preprint

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“…Furthermore, reliance on data from a driving simulator may fail to fully capture the complexities and variabilities of real-world driving conditions, potentially limiting the model's applicability in real-world scenarios. In (Varghese et al, 2021), an integrated system using machine learning to detect driver drowsiness and alcohol intoxication is proposed, addressing key factors contributing to road accidents. The system employs an MQ-3 sensor for alcohol detection and a webcam for non-intrusive drowsiness detection through facial features.…”

Section: Introductionmentioning

confidence: 99%

A semi-supervised anomaly detection strategy for drunk driving detection: a feasibility study

Harrou,

Kini,

Madakyaru

et al. 2024

Front. Sens.

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Drunk driving poses a significant threat to road safety, necessitating effective detection methods to enhance preventive measures and ensure the well-being of road users. Recognizing the critical importance of identifying drunk driving incidents for public safety, this paper introduces an effective semi-supervised anomaly detection strategy. The proposed strategy integrates three key elements: Independent Component Analysis (ICA), Kantorovitch distance (KD), and double Exponentially Weighted Moving Average (DEWMA). ICA is used to handle non-gaussian and multivariate data, while KD is used to measure the dissimilarity between normal and abnormal events based on ICA features. The DEWMA is applied to KD charting statistics to detect changes in data and uses a nonparametric threshold to improve sensitivity. The primary advantage of this approach is its ability to perform anomaly detection without requiring labeled data. The study also used XGBoost for the later calculation of the SHAP (SHapley Additive exPlanations) values to identify the most important variables for detecting drunk driving behavior. The approach was evaluated using publicly available data from gas and temperature sensors, as well as digital cameras. The results showed that the proposed approach achieved an F1-score of 98% in detecting the driver’s drunk status, outperforming conventional PCA-based and ICA-based methods.

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Driver Drowsiness Detection System Based on Yawn Detection

Kamble

2024

Algorithms for Intelligent Systems

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An Integrated Framework for Driver Drowsiness Detection and Alcohol Intoxication using Machine Learning

Cited by 15 publications

References 7 publications

A Lightweight In-Vehicle Alcohol Detection using Smart Sensing and Supervised Learning

A Lightweight In-Vehicle Alcohol Detection using Smart Sensing and Supervised Learning

A semi-supervised anomaly detection strategy for drunk driving detection: a feasibility study

Driver Drowsiness Detection System Based on Yawn Detection

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