International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 11 Issue: 03 | Mar 2024
p-ISSN: 2395-0072
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A9G-ENABLED ACCIDENT DETECTION AND RESPONSE SYSTEM FOR ROAD SAFETY Vignesh M1, Muthukumar K2 1Industrial Safety Engineering 2Assistant Professor, Mechanical Department
3Bannari Amman Institute of Technology, Sathyamangalam
---------------------------------------------------------------------***--------------------------------------------------------------------identified, the system automatically transmits an SMS, Abstract - Road accidents pose a global public health
encompassing the accident's precise location, date, and time, to the nearest police station. This swift communication empowers emergency services to respond promptly, potentially saving countless lives in the process.
concern, leading to significant loss of life, primarily due to delayed emergency response. In response to this critical issue, we have developed an innovative safety solution that leverages the Arduino Nano, MPU6050 sensor, and A9G GPS module. This system enhances road safety by autonomously detecting accidents and promptly alerting emergency services. The MPU6050 continuously monitors the device's motion, identifying abrupt changes that may indicate accidents, while the A9G GPS module provides real-time location data. In the event of an accident, the system triggers an SMS notification to the nearest police station, including precise GPS coordinates, date, and time, enabling faster emergency response. Extensive real-world testing has validated the system's effectiveness. With the potential to save countless lives, our solution holds promise for improving road safety and reducing fatalities.
In this paper, we delve into the architecture, functionality, and real-world testing of our device, showcasing its effectiveness in detecting accidents and expediting emergency response. Our aim is to reduce fatalities resulting from road accidents and enhance overall road safety. With the potential to revolutionize accident response protocols, we believe that our system holds the promise of making our roads safer and preserving lives on a global scale.
2. FALL DETECTION SYSTEMS: A REVIEW OF EXISTING APPROACHES
Key Words: MPU6050, A9G, Emergency Services, Helmet, SMS,Detection, Location
A. Vision-based approach One approach to fall detection is to use cameras to monitor the activity of the person. If a fall is detected, the camera can be used to capture images of the event. These images can then be used to assess the severity of the fall and to determine if medical assistance is required [16]. However, vision-based approaches can be expensive and difficult to use in low-light conditions.
1.INTRODUCTION Road traffic accidents represent a global epidemic, claiming thousands of lives daily and inflicting immeasurable suffering. It's a sobering reality that, on average, a person loses their life in a road accident every five minutes. The severity of this problem is amplified by the disheartening fact many of these accidents occur within proximity to hospitals and emergency services, yet victims are often left unaided, as every second counts in the race against time.
B. Machine learning-based approach Another approach to fall detection is to use machine learning algorithms to classify falls from other daily activities [17]. This approach requires a large dataset oflabeled data, which can be difficult to obtain. However, machine learning-based approaches can be very accurateand can be used in a variety of settings.
The human brain can endure a mere 3 to 6 minutes without oxygen, emphasizing the critical importance of swift emergency medical attention. Addressing this dire need for expedited accident response, we present an innovative solution: an A9G-Enabled Accident Detection and Response System. Building upon existing protective headgear, our device seamlessly integrates advanced technologies such as the Arduino Nano, MPU6050 sensor, and the A9G GPS module to detect accidents in real time and connect with emergency services within moments.
C. Vibrations and sound-based approach A third approach to fall detection is to use vibrations and sound to detect falls. This approach is based on the principle that a fall will generate a characteristic pattern of vibrations and sound. These vibrations and sounds can be captured by sensors and then analyzed to determine if a fall has occurred [18]. However, this approach can be sensitive to noise and can be difficult to use in noisy environments.
The core concept revolves around the swift detection of accidents and the immediate relay of critical information to nearby law enforcement agencies. Once an accident is
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