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@article{164030,
        author = {A. venka reddy and Ch.Prasanna and B.Bhagya Lakshmi and D.Muni Lakshmi and V.Venkata Balaji and A. Gnanendra srinivas},
        title = {EMBEDDED BASED SMART ACCIDENT PRE ALERT, POST-ALERT AND PREVENTION SYSTEM  WITH MACHINE LEARNING},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {10},
        number = {12},
        pages = {268-271},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=164030},
        abstract = {This project presents the design and implementation of an embedded-based smart accident pre- alert and prevention system integrated with machine learning capabilities. The system is intended for vehicles to monitor various parameters including driver's eyeblinks, heart rate, alcohol levels, accident detection vehicle positioning, and environmental factors to enhance road safety and prevent accidents.
The system incorporates an raspberry pi controller interfaced with multiple sensors, including USB camera, heart rate sensor, alcohol sensor, MEMS sensor, GPS module, ultrasonic sensor, and GSM module. The raspberry pi processes data from these sensors to monitor the driver's condition, vehicle status and surrounding environment. 
The camera continuously detects the driver's eyes, providing insights into their alertness and drowsiness levels. The heart rate sensor monitors the driver's heart rate, offering additional indicators of stress or fatigue. The alcohol sensor measures the alcohol content in the driver's breath, alerting if the driver is under the influence. In the event of a potential accident, the MEMS sensor detects sudden changes in vehicle acceleration or orientation, indicating a collision. Simultaneously, the ultrasonic sensor detects the proximity of nearby vehicles, enhancing collision detection. 
},
        keywords = {},
        month = {},
        }