WakeSense Portable IoT-Based Anti-Drowsiness Alert System for Drivers

One of the main contributing factors to traffic accidents is driver fatigue, particularly during lengthy drives. In order to improve road safety in cars without integrated monitoring systems, this project suggests a portable driver drowsiness detection system. The system uses a webcam and AI-based facial analysis to track head movements, eye closure, and yawning in order to identify drowsiness. A buzzer and seat vibrations alert the driver upon detection, and the system lowers the speed of the vehicle to avoid collisions. The system automatically sounds an alert if the driver's face is hidden by sunglasses or poor vision. Furthermore, when an accelerometer sensor detects a crash, the Raspberry Pi uses Telegram to send an emergency SOS with GPS location. This system is perfect for improving safety in older cars because it is affordable, flexible, and simple to install. The system offers an effective real-time safety solution for long-distance travel by combining automated speed control, AI-based detection, and emergency alerting.

Authors

Publication Details

Journal
International Journal For Multidisciplinary Research
Published
2025-04-25
DOI
https://doi.org/10.36948/ijfmr.2025.v07i02.42622
Primary Topic
Sleep and Work-Related Fatigue
Type
article
Field-Weighted Citation Impact
0.00
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article

WakeSense Portable IoT-Based Anti-Drowsiness Alert System for Drivers

Chinthakunta Sasikala, V Karpagam, Rethick Kumar K. J., C R Shiraptinath et al.
International Journal For Multidisciplinary Research
Sleep and Work-Related Fatigue
article

WakeSense Portable IoT-Based Anti-Drowsiness Alert System for Drivers

Chinthakunta Sasikala, V Karpagam, Rethick Kumar K. J., C R Shiraptinath, Venkata Lakshmi S
article en

Abstract

One of the main contributing factors to traffic accidents is driver fatigue, particularly during lengthy drives. In order to improve road safety in cars without integrated monitoring systems, this project suggests a portable driver drowsiness detection system. The system uses a webcam and AI-based facial analysis to track head movements, eye closure, and yawning in order to identify drowsiness. A buzzer and seat vibrations alert the driver upon detection, and the system lowers the speed of the vehicle to avoid collisions. The system automatically sounds an alert if the driver's face is hidden by sunglasses or poor vision. Furthermore, when an accelerometer sensor detects a crash, the Raspberry Pi uses Telegram to send an emergency SOS with GPS location. This system is perfect for improving safety in older cars because it is affordable, flexible, and simple to install. The system offers an effective real-time safety solution for long-distance travel by combining automated speed control, AI-based detection, and emergency alerting.

International Journal For Multidisciplinary ResearchVol. 7(2)
Openalex Percentile: Top 92%
Sleep and Work-Related Fatigue
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