Journal of ISMAC
IRO Journals
Volume 7 — Issue 3 — September 2025

Preventing Road Crashes with IoT Monitoring of Driver Fatigue in Real-Time

https://doi.org/10.36548/jismac.2025.3.001
Pages: 231-256
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Abhijan S. Kashyap
Department of Master of Computer Application, Acharya Institute of Technology
Sumit Singha Chowdhury
Department of Master of Computer Application, Acharya Institute of Technology
Ananya V. Hegde
Department of Master of Computer Application, Acharya Institute of Technology
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How to Cite

Kashyap, Abhijan S., Sumit Singha Chowdhury, and Ananya V. Hegde. 2025. “Preventing Road Crashes With IoT Monitoring of Driver Fatigue in Real-Time”. Journal of ISMAC 7 (3): 231-56. https://doi.org/10.36548/jismac.2025.3.001.
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Keywords

— IoT
— ESP32
— Infrared Eye Blink Sensor
— MPU6050
— IMU
— Head Movement Analysis
— Fatigue Detection
— Road Safety
— Human Factors
— Low-Cost Embedded System
Published: 25-07-2025

Abstract

Road safety is of utmost importance for national and regional connectivity and economic development; however, as more vehicles occupy our roads, the rise in road traffic accidents has led to reported fatalities (World Health Organization) of more than 1.3 million each year. Among the critical and commonly overlooked causal factors for road traffic accidents is driver fatigue, which detrimentally influences one's ability to react and maintain alertness. This paper presents a technically novel, non-intrusive, and low-cost Internet of Things (IoT)-based driver drowsiness detection system. While previous research has primarily utilized camera-based or wearable sensor solutions, this system utilizes an ESP32 microcontroller, equipped with an infrared (IR) eye-blink sensor and an MPU6050 Inertial Measurement Unit (IMU), to identify eye closure for extended durations and incorrect head movements. When drowsiness is identified for more than 5 seconds, the buzzer and LED provide real-time alerts to the driver, and the event is logged in the Firebase Real-time Database. Additionally, the system is accessible from a purpose-designated web dashboard, allowing the supervisor or authority to monitor the driver remotely. The system was tested in a simulated driving environment with human participants to evaluate persistent alertness and accuracy of detection. The results of this project revealed a detection accuracy of 90%, alerts issued in under one second, and anecdotal feedback from users confirmed that the interrupting mechanism of two alerts was successful in regaining the attention of the driver. The innovative element of this project is hybrid IR-IMU sensing, cloud integration, and a responsive feedback loop, providing a scalable and low-cost solution for reducing road accidents or incidents related to driver fatigue.

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