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Stream/Area:
-
Subject:
-
Paper ID:
10.55041/IJSREM32360
Volume:
8 |
4 |
Apr 2024
Pages:
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
The fatigue detection system is a critical application aimed at enhancing safety in various domains by addressing the risks associated with drowsiness related incidents. Leveraging advanced technologies such as machine learning, computer vision, and real-time communication, the system offers a comprehensive solution for monitoring and alerting users to signs of fatigue in real-time. Through the integration of pre-trained models, such as the shape_predictor_68_face_landmarks.dat, the system accurately detects facial landmarks and tracks eye movements, enabling the identification of drowsiness indicators. Upon detection, the system generates alerts via email and WhatsApp, accompanied by audible alarm buzzers to prompt immediate user intervention. The inclusion of live location information in alerts provides additional context, facilitating timely assistance and support. Furthermore, the system’s architecture allows for future enhancements, including the integration of multi-sensor data fusion techniques and collaboration with industry stakeholders to promote standardized protocols and widespread adoption. Overall, the fatigue detection system represents a significant step towards mitigating the risks of drowsinessrelated accidents and improving safety across various applications
KEYWORDS
Publication Date
12/31/45410
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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