Preventing Impaired Driving Using IoT on Steering Wheels Approach
DOI: 10.28991/hij-2024-05-02-012
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Summary
This paper addresses the critical public health and safety issue of alcohol-impaired driving, which significantly contributes to road traffic accidents and mortality globally. The authors argue that relying solely on gas sensors for alcohol detection is insufficient due to potential inaccuracies and environmental interference. To mitigate this risk, the study proposes an Internet of Things (IoT)-based tool integrated into a vehicle’s steering wheel that simultaneously monitors a driver’s breath alcohol concentration (BrAC) and heart rate. The motivation stems from the physiological link between alcohol consumption and increased heart rate, suggesting that combining these two metrics provides a more comprehensive assessment of driver impairment than alcohol detection alone. The research employs the C2A2 (Capture, Communicate, Analyze, Act) IoT lifecycle methodology to design and test a prototype system. The hardware setup includes an Arduino Uno microcontroller, an MQ3 alcohol sensor, a MAX30102 heart rate sensor, a GSM/GPS module (SIM9000A), an ESP8266 Wi-Fi module, and a DC motor representing the vehicle engine. The system captures real-time data from the sensors, communicates it to a central server via Wi-Fi, and analyzes it against predetermined thresholds. These thresholds are based on legal BrAC limits (specifically 0.22 mg/L for Malaysia) and physiological heart rate limits (exceeding 100 beats per minute or a significant rise above resting rate). If both alcohol presence and elevated heart rate are detected, the system acts by disabling the DC motor (simulating engine shutdown) and sending an SMS with the driver’s GPS location to a registered emergency contact. The results demonstrate the successful integration and functionality of the prototype. Testing involved simulating alcohol presence using perfume (38% alcohol) and hand sanitizer (90% ethanol) near the MQ3 sensor, and inducing elevated heart rates through cardio exercise. The MQ3 sensor effectively detected alcohol concentrations, with analog readings correlating to BrAC values. In integration tests, when both the heart rate exceeded 100 bpm and the alcohol sensor reading surpassed the threshold (analog value ≥ 550), the system correctly triggered the safety measures: the DC motor stopped, and an SMS notification was successfully transmitted to the emergency contact. The prototype was housed in a PVC enclosure with LED indicators to provide visual feedback to the driver, demonstrating its potential as an aftermarket accessory. The significance of this work lies in its potential to enhance road safety by providing a non-invasive, real-time method for detecting impaired drivers before they operate a vehicle. By combining BrAC and heart rate monitoring, the system aims to reduce false positives and improve detection accuracy compared to single-sensor approaches. The authors conclude that while the prototype is promising, further development is required to address limitations such as sensor noise from vehicle vibrations, individual physiological variations, and ergonomic integration into existing steering wheels. The study suggests that such IoT-based countermeasures could be valuable tools for researchers and policymakers in efforts to reduce alcohol-related accidents, provided that issues regarding public acceptance, regulatory compliance, and robust sensor calibration are addressed in future iterations.
Key finding
The prototype IoT system successfully disabled the simulated vehicle engine and sent emergency notifications when it detected simultaneous elevations in heart rate and breath alcohol concentration.
Methodology
lab_experiment
Provenance
The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed. Discovered via scout_discovery on 2026-05-08.
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | partial | scout | — | — | 2 | 2026-05-08 |
| archive | success | unpaywall | — | — | 1 | 2026-06-04 |
| extract | success | cached | — | — | 3 | 2026-06-10 |
| clean | success | clean | — | — | 1 | 2026-06-04 |
| chunk | success | chunk | — | — | 1 | 2026-06-04 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-04 |
| enrich | success | semantic_scholar | — | — | 2 | 2026-06-04 |
| promote | success | — | — | — | 1 | 2026-06-04 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 2 | 2026-06-10 |
| tag | success | vector_similarity | — | — | 15 | 2026-06-11 |
| verify | partial | — | — | — | 2 | 2026-06-10 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified_with_issues.
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- Methodological Resource: validation psychometrics