Detection of functional state after alcohol consumption by classification and machine learning technics

Evin, Morgane; Taillard, Joffrey; Fuente, Hugo Loeches de la; Galy, Édith; Berthelon, Catherine

doi:10.1109/bibm.2018.8621310

Cited by 6 publications

(4 citation statements)

References 28 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 goal of the study presented in [36] was to check how well different classifications and machine learning techniques could predict alcohol consumption and related functional states. The data was analyzed in 10-second time frames with no superposition or gaps.…”

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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“…These parameters were used to calibrate machine learning models for predicting blood alcohol content (BAC) and functional states (e.g., performance and alertness). Experimental results demonstrate that the suggested method is effective, with an accuracy of 0.714 for BAC detection and an accuracy of 0.877 to 0.907 for functional state detection [76]. Moreover, a sobriety detection tool that employs using a PPG signal to detect an individual's BAC level shows that the tool is feasible for BAC level identification has been achieved with an accuracy of 85% [77].…”

Section: Drunk Driving Monitoringmentioning

confidence: 89%

Physiological-based Driver Monitoring Systems: A Scoping Review

et al. 2022

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A physiological-based driver monitoring system (DMS) has attracted research interest and has great potential for providing more accurate and reliable monitoring of the driver’s state during a driving experience. Many driving monitoring systems are driver behavior-based or vehicle-based. When these non-physiological based DMS are coupled with physiological-based data analysis from electroencephalography (EEG), electrooculography (EOG), electrocardiography (ECG), and electromyography (EMG), the physical and emotional state of the driver may also be assessed. Drivers’ wellness can also be monitored, and hence, traffic collisions can be avoided. This paper highlights work that has been published in the past five years related to physiological-based DMS. Specifically, we focused on the physiological indicators applied in DMS design and development. Work utilizing key physiological indicators related to driver identification, driver alertness, driver drowsiness, driver fatigue, and drunk driver is identified and described based on the PRISMA Extension for Scoping Reviews (PRISMA-Sc) Framework. The relationship between selected papers is visualized using keyword co-occurrence. Findings were presented using a narrative review approach based on classifications of DMS. Finally, the challenges of physiological-based DMS are highlighted in the conclusion. Doi: 10.28991/CEJ-2022-08-12-020 Full Text: PDF

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A hybrid approach for classification and identification of iris damaged levels of alcohol drinkers

Jayadev¹,

Bellary²

2022

Journal of King Saud University - Computer and Information Scie

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Detection of functional state after alcohol consumption by classification and machine learning technics

Cited by 6 publications

References 28 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

Physiological-based Driver Monitoring Systems: A Scoping Review

A hybrid approach for classification and identification of iris damaged levels of alcohol drinkers

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