A Case-Based Classification for Drivers’ Alcohol Detection Using Physiological Signals

Rahman, Hamidur; Ahmed, Mobyen Uddin; Begum, Shahina; Hök, Bertil

doi:10.1007/978-3-319-51234-1_4

Cited by 4 publications

(5 citation statements)

References 13 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%

“…A case-based classification method for alcohol detection utilizing physiological indicators was proposed in [39]. Four physiological measures are used in a Case-based reasoning system to detect alcoholic state, including Skin Conductance, Finger Temperature, Respiration Rate, and Heart Rate Variability.…”

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

View full text Add to dashboard Cite

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“…Several approaches for intoxication detection while driving have been proposed. These include contact and remote breath analysis [13,14], skin conductivity analysis [15], heart rate variability [16], gaze behaviour and head movements [17], driving behaviour [18], or a combination of the above. Most of the methods require additional and sometimes obtrusive sensors to be installed in the car, or are not feasible in case of automated driving.…”

Section: Introductionmentioning

confidence: 99%

Practical aspects of measuring camera‐based indicators of alcohol intoxication in manual and automated driving

Zemblys,

Ahlström,

Kircher

et al. 2024

IET Intelligent Trans Sys

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Camera‐based Driver Monitoring Systems (DMS) have the potential to exploit eye tracking correlates of alcohol intoxication to detect drunk driving. This study investigates how glance, blink, saccade, and fixation metrics are affected by alcohol, and whether possible effects remain stable across three different camera setups, as well as when the driver is out‐of‐the‐loop during level 4 automated driving (Wizard‐of‐Oz setup). Thirty‐five participants drove on a test track first sober and then with increasing intoxication levels reaching a breath alcohol concentration (BrAC) of 1‰. Linear Mixed‐Effects Regression analyses showed that with increasing intoxication levels, eye blinks became longer and slower, glances and fixations became fewer and longer, and more attention was directed to the road area, at the expense of more peripheral areas. Fixation and blink metrics were more robust to changes in automation mode, whereas glance‐based metrics were highly context dependent. Not all effects of alcohol intoxication could be measured with all eye tracking setups, where one‐camera systems showed lower data availability and higher noise levels compared to a five‐camera system. This means that lab findings based on higher quality eye tracking data might not be directly applied to production settings because of hardware limitations.

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“…Existing studies utilize two major categories of data inputs, namely physiological indicators and driving performance. The former includes measurements of driving conditions, e.g., body movements [6]- [10], organ activities [11], or combined [12]. Nevertheless, most physiological indicators require specific equipment (e.g., electrocardiogram machines) to be on body for tracking, which is not straightforward to deploy and may cause additional distractions.…”

Section: Introductionmentioning

confidence: 99%

An Automated Machine Learning (AutoML) Method for Driving Distraction Detection Based on Lane-Keeping Performance

Chai¹,

Lu²,

Jiang³

et al. 2021

Preprint

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With the enrichment of smartphones, driving distractions caused by phone usages have become a threat to driving safety. A promising way to mitigate driving distractions is to detect them and give real-time safety warnings. However, existing detection algorithms face two major challenges, low user acceptance caused by in-vehicle camera sensors, and uncertain accuracy of pre-trained models due to drivers' individual differences. Therefore, this study proposes a domain-specific automated machine learning (AutoML) to self-learn the optimal models to detect distraction based on lane-keeping performance data. The AutoML integrates the key modeling steps into an auto-optimizable pipeline, including knowledge-based feature extraction, feature selection by recursive feature elimination (RFE), algorithm selection, and hyperparameter auto-tuning by Bayesian optimization. An AutoML method based on XGBoost, termed AutoGBM, is built as the classifier for prediction and feature ranking. The model is tested based on driving simulator experiments of three driving distractions caused by phone usage: browsing short messages, browsing long messages, and answering a phone call. The proposed AutoGBM method is found to be reliable and promising to predict phone-related driving distractions, which achieves satisfactory results prediction, with a predictive power of 80% on group level and 90% on individual level accuracy. Moreover, the results also evoke the fact that each distraction types and drivers require different optimized hyperparameters values, which reconfirm the necessity of utilizing AutoML to detect driving distractions. The purposed AutoGBM not only produces better performance with fewer features; but also provides data-driven insights about system design.

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A Case-Based Classification for Drivers’ Alcohol Detection Using Physiological Signals

Cited by 4 publications

References 13 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

Practical aspects of measuring camera‐based indicators of alcohol intoxication in manual and automated driving

An Automated Machine Learning (AutoML) Method for Driving Distraction Detection Based on Lane-Keeping Performance

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