International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 04 | Apr 2025
p-ISSN: 2395-0072
www.irjet.net
SMART ANTI SLEEP ALARM FOR DRIVERS B.KALAIVANI1, B.VAIDEHI2, K.MUNEESWARI3, B.MURUGALAKSHMI4 1, 2 Assistant Professor, Dept. of Computer Science, Sri S.Ramasamy Naidu Memorial College (Affiliation of Madurai
Kamaraj University), Sattur-626 203, Tamilnadu, India
3, 4 UG Student, Dept. of Computer Science, Sri S.Ramasamy Naidu Memorial College (Affiliation of Madurai Kamaraj
University), Sattur-626 203, Tamilnadu, India ---------------------------------------------------------------------***--------------------------------------------------------------------
Abstract - With the increasing number of traffic accidents worldwide, detecting driver drowsiness has become a critical area of research to enhance road safety. This project presents a real-time driver drowsiness detection system designed to prevent accidents caused by fatigue. The system utilizes an eye blink sensor to monitor the motorist’s eye state, triggering an alert if prolonged eye closure is detected. The device, implemented using an Arduino microcontroller, integrates an infrared (IR) sensor to classify eye blinks and activate a buzzer when necessary. Additionally, a relay mechanism is employed to control a DC motor, simulating vehicle response to drowsiness detection. The proposed system is portable, cost-effective and capable of immediate intervention, significantly reducing the risk of fatigue-induced accidents. By continuously analyzing the driver’s eye activity, this work aims to contribute to safer road conditions and minimize accident rates.
The integration of IoT with smart anti-sleep alarms enhances real-time data processing and wireless connectivity, ensuring efficient fatigue detection and response mechanisms. IoT-enabled solutions allow for continuous monitoring of drivers by collecting and analyzing data from various sensors in real-time (7). Cloudbased platforms further improve the efficiency of these systems by providing remote monitoring capabilities and automated alerts (8). By leveraging IoT and machine learning, modern anti-sleep alarm systems can adapt to individual driver patterns, offering personalized fatigue detection and warning strategies (9).
Keywords: IoT, IR Rays, Arduino, Microcontroller, Road Safety
This work presents the development of an intelligent Smart Anti-Sleep Alarm System designed to detect early signs of fatigue and issue immediate alerts to drivers. The system employs a combination of sensors, including an accelerometer buzzer and relay circuits, to monitor driver behavior and activate an alarm upon detecting drowsiness. By implementing an effective and reliable anti-sleep mechanism, this work aims to contribute to reducing fatigue-related accidents and improving road safety.
1. INTRODUCTION
2. Related Works
Road safety remains a global concern, with drowsy driving being a significant contributor to traffic accidents. Studies indicate that driver fatigue impairs reaction time, decision-making, and alertness, increasing the likelihood of collisions (1). Fatigue-related accidents have become a pressing issue, necessitating the development of real-time monitoring and intervention systems to enhance driver alertness and prevent mishaps (2). With advancements in intelligent transport systems and Internet of Things (IoT) applications, innovative solutions are being explored to mitigate the risks associated with drowsy driving (3).
Recent advancements in anti-sleep alarm systems have leveraged machine learning, wearable technology, and IoT integration to improve fatigue detection accuracy. Several studies have explored different methodologies to enhance driver safety through early drowsiness detection. Chen et al. (2023) introduced a deep learning-based drowsiness detection system that utilized convolutional neural networks (CNNs) to analyze facial expressions and eye movements. Their model demonstrated high accuracy in controlled environments but faced challenges in real-world applications due to varying lighting conditions and occlusions [10].
Various technologies have been implemented to detect driver fatigue, including image processing, bio signal monitoring, and steering behavior analysis. Computer vision- based systems use eye-tracking methods to assess drowsiness by monitoring blink rates and head movements (4). Wearable sensors embedded in headgear or eyeglasses track physiological parameters such as heart rate and brain activity to detect sleepiness (5). Additionally, machine learning algorithms integrated with vehicle-based systems analyze steering patterns and vehicle deviations to predict drowsiness and issue timely warnings (6).
Wang et al. (2023) proposed a hybrid approach combining EEG signals with an image-based detection system, improving real-time monitoring capabilities while addressing false positive concerns [11]. Ahmed et al. (2024) focused on integrating electrocardiogram (ECG) sensors with IoT-based alert systems. Their findings indicated that heart rate variability serves as a reliable indicator of fatigue, enabling real-time notifications through cloud-based platforms. However, the need for non-intrusive ECG sensors was highlighted to enhance user comfort [12].
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