Big Data Analytics for Improved Risk Management and Customer Segregation in Banking Applications
Volume-3 | Issue-3
Design of Deep Learning Algorithm for IoT Application by Image based Recognition
Volume-3 | Issue-3
Analysis of Serverless Computing Techniques in Cloud Software Framework
Volume-3 | Issue-3
Health Record Management System – A Web-based Application
Volume-3 | Issue-4
A Novel Signal Processing Based Driver Drowsiness Detection System
Volume-3 | Issue-3
IoT Based Monitoring and Control System using Sensors
Volume-3 | Issue-2
Secure Data Sharing Platform for Portable Social Networks with Power Saving Operation
Volume-3 | Issue-3
Review of Internet of Wearable Things and Healthcare based Computational Devices
Volume-3 | Issue-3
Stock Index Prediction with Financial News Sentiments and Technical Indicators
Volume-4 | Issue-3
Hybrid Framework on Automatic Detection and Recognition of Traffic Display board Signs
Volume-3 | Issue-3
Suspicious Human Activity Detection System
Volume-2 | Issue-4
ROBOT ASSISTED SENSING, CONTROL AND MANUFACTURE IN AUTOMOBILE INDUSTRY
Volume-1 | Issue-3
EFFICIENT RESOURCE ALLOCATION AND QOS ENHANCEMENTS OF IOT WITH FOG NETWORK
Volume-1 | Issue-2
Live Streaming Architectures for Video Data - A Review
Volume-2 | Issue-4
IoT Based Monitoring and Control System using Sensors
Volume-3 | Issue-2
Big Data Analytics for Improved Risk Management and Customer Segregation in Banking Applications
Volume-3 | Issue-3
A Novel Signal Processing Based Driver Drowsiness Detection System
Volume-3 | Issue-3
IoT BASED AIR AND SOUND POLLUTION MONITIORING SYSTEM USING MACHINE LEARNING ALGORITHMS
Volume-2 | Issue-1
Analysis of Serverless Computing Techniques in Cloud Software Framework
Volume-3 | Issue-3
Hybrid Intrusion Detection System for Internet of Things (IoT)
Volume-2 | Issue-4
Volume - 6 | Issue - 1 | march 2024
Published
30 April, 2024
Detecting road accidents promptly is crucial for minimizing casualties and property damage worldwide. The proposed system, comprising hardware and a mobile application, automatically identifies and reports accidents to emergency services. It also employs a facial recognition system to detect driver drowsiness, enhancing accident prevention measures. By leveraging sensor technologies, cellular networks, and advanced detection algorithms, the proposed system analyzes data from accelerometers, Global System for Mobile Communication (GSM), and Global Positioning System (GPS) sensors. Originally designed for vehicles, it can be easily adapted for deployment in various settings such as factories and construction sites with minor adjustments. The system continuously monitors the driver's facial expressions and activities using sensors. When drowsiness is detected, it activates a buzzer, and in the event of a crash, it alerts the driver to prevent false alarms while simultaneously notifying the rescue center if a genuine crash has occurred. This integrated approach enhances safety and optimizes emergency response efforts. The Arduino microcontroller, equipped with an accelerometer, identifies sudden changes in motion like acceleration and rotation to assess impacts against predefined thresholds. Furthermore, GPS functionality accurately determines the vehicle's location at the time of the accident, while GSM enables seamless communication with rescue centers through notifications.
KeywordsAccelerometer Arduino Crash Detection Drowsiness GPS GSM
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