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@article{202804,
        author = {Piyush Peraspure and Vaishali Kolhe and Sachchaita Hangloo and Yashraj Jagtap and Aamodi Patole},
        title = {Iot Based - Safe Drive Drowsiness Detection System},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {8969-8972},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=202804},
        abstract = {Driver drowsiness is one of the most important reasons for road accidents around the world, especially while traveling long distances and at night. Continuous monitoring of the driver’s alertness is required for preventing accidents. Conventional techniques for fatigue detection using physiological sensors are invasive, costly, and not suitable for real-time implementation in vehicles. This paper proposes a non-invasive IoT-based driver drowsiness detection system using computer vision and facial landmark analysis. The proposed system uses the MediaPipe FaceMesh model for identifying the important facial landmarks around the eye and mouth regions. Eye Aspect Ratio (EAR) and Mouth Aspect Ratio (MAR) are calculated continuously to identify the sustained closure of eyes and yawning behavior. Moreover, the estimation of head tilt, as well as the monitoring of head stillness, is combined for enhanced robustness. A laughter/talking filter is used to avoid false alarms when the mouth is normally moving. When drowsiness is detected, the system sends an alert and records events for later analysis. Future improvements will include implementation on a hardware kit based on Raspberry Pi, with emergency notification and location sharing using GSM. The system offers an effective and affordable solution for smart driver safety systems.},
        keywords = {Driver Drowsiness Detection, MediaPipe FaceMesh, EAR, MAR, Head Pose Estimation, IoT Safety System, Raspberry Pi.},
        month = {May},
        }