ArticleSensors (Basel, Switzerland)2021
In-Vehicle Alcohol Detection Using Low-Cost Sensors and Genetic Algorithms to Aid in the Drinking and Driving Detection.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed, 33 citations in OpenAlex.
- Holey Carbon Nanohorns-Based Nanohybrid as Sensing Layer for Resistive Ethanol Sensor.Sensors (Basel, Switzerland) · 2025Article
- Multimodal driver emotion recognition using motor activity and facial expressions.Frontiers in artificial intelligence · 2024Article
- Development and Performance Evaluation of an IoT-Integrated Breath Analyzer.International journal of environmental research and public health · 2023Article
- Driver Identification Using Statistical Features of Motor Activity and Genetic Algorithms.Sensors (Basel, Switzerland) · 2023Article
- An Intelligent Online Drunk Driving Detection System Based on Multi-Sensor Fusion Technology.Sensors (Basel, Switzerland) · 2022Article
- From 0D to 2D: N-doped carbon nanosheets for detection of alcohol-based chemical vapours.RSC advances · 2022Article
- EEG-Based Alcohol Detection System for Driver Monitoring.International journal of psychological researchArticle
Corrections and comments
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Authors and funding
11 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Worldwide, motor vehicle accidents are one of the leading causes of death, with alcohol-related accidents playing a significant role, particularly in child death. Aiming to aid in the prevention of this type of accidents, a novel non-invasive method capable of detecting the presence of alcohol inside a motor vehicle is presented. The proposed methodology uses a series of low-cost alcohol MQ3 sensors located inside the vehicle, whose signals are stored, standardized, time-adjusted, and transformed into 5 s window samples. Statistical features are extracted from each sample and a feature selection strategy is carried out using a genetic algorithm, and a forward selection and backwards elimination methodology. The four features derived from this process were used to construct an SVM classification model that detects presence of alcohol. The experiments yielded 7200 samples, 80% of which were used to train the model. The rest were used to evaluate the performance of the model, which obtained an area under the ROC curve of 0.98 and a sensitivity of 0.979. These results suggest that the proposed methodology can be used to detect the presence of alcohol and enforce prevention actions.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.