Applications of internet of things for monitoring drivers-a comprehensive study
DOI: 10.11591/ijeecs.v25.i3.pp1599-1606
archive: archived pipeline: cataloged verified
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Summary
This paper addresses the critical safety issues associated with sudden health abnormalities and alcohol consumption while driving, which are significant contributors to road accidents. Motivated by World Health Organization data linking sudden health complications and drunk driving to severe injuries and fatalities, the authors propose an Internet of Things (IoT) based active monitoring tool. The system aims to enhance road safety by continuously tracking a driver’s physiological status and alerting emergency contacts when vital thresholds are breached. The methodology involves the development of a hardware prototype designed to monitor heart rate and alcohol concentration in real-time. The system utilizes an Arduino UNO microcontroller as the central processing unit, integrated with specific sensors and communication modules. For health monitoring, a pulse sensor detects the driver’s heart rate, while an MQ3 sensor measures alcohol concentration in the breath. The setup also includes a GSM module for transmitting alerts, a buzzer for local warnings, and LEDs for visual status indication. The operational logic dictates that if heart rate readings become abnormal or alcohol levels exceed a predefined threshold, the system triggers an alarm and sends the data to a designated emergency contact and a cloud server for further analysis. The results demonstrate that the prototype functions satisfactorily in a laboratory-controlled environment. The system successfully monitored heart rates and detected alcohol presence with a response time of 1 to 3 seconds for each measurement procedure. Visual indicators provided immediate feedback: a flashing white light indicated normal heart rate, while a red light signaled abnormalities. For alcohol detection, green, blue, and yellow lights corresponded to normal, slightly concentrated, and heavily concentrated levels, respectively. When the yellow light indicated high alcohol concentration, the system correctly triggered the buzzer and transmitted the alert. The study confirms that the cost-effective sensors used produced adequate output for the intended monitoring tasks. The significance of this work lies in its demonstration of a feasible, low-cost IoT solution for proactive driver safety management. By integrating real-time health and alcohol monitoring with cloud connectivity and emergency notification, the tool offers a practical approach to preventing accidents caused by medical emergencies or impaired driving. The authors conclude that while the prototype is currently validated in a laboratory setting, future work will focus on packaging the device for in-vehicle testing to assess performance under real-world driving conditions and identify potential factors influencing sensor readings.
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | Crossref | — | — | 1 | 2026-06-25 |
| archive | success | canonical_url | — | — | 1 | 2026-06-26 |
| extract | success | cached | — | — | 2 | 2026-06-26 |
| clean | success | clean | — | — | 1 | 2026-06-25 |
| chunk | success | chunk | — | — | 1 | 2026-06-25 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-25 |
| promote | success | — | — | — | 1 | 2026-06-25 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 1 | 2026-06-26 |
| tag | success | vector_similarity | — | — | 6 | 2026-06-25 |
| verify | success | — | — | — | 1 | 2026-06-26 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-26; verification: verified.
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Information type
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- Empirical Findings: physiological data
- Methodological Resource: tool software