Journal ArticleDOI
An IoT-Based Vehicle Accident Detection and Classification System Using Sensor Fusion
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TLDR
This work presents an IoT-based automotive accident detection and classification (ADC) system, which uses the fusion of smartphone’s built-in and connected sensors not only to detect but also to report the type of accident.Abstract:
Road accidents are a leading cause of death and disability among youth. Contemporary research on accident detection systems is focused on either decreasing the reporting time or improving the accuracy of accident detection. Internet-of-Things (IoT) platforms have been utilized considerably in recent times to reduce the time required for rescue after an accident. This work presents an IoT-based automotive accident detection and classification (ADC) system, which uses the fusion of smartphone’s built-in and connected sensors not only to detect but also to report the type of accident. This novel technique improves the rescue efficacy of various emergency services, such as emergency medical services (EMSs), fire stations, towing services, etc., as knowledge about the type of accident is extremely valuable in planning and executing rescue and relief operations. The emergency assistance providers can better equip themselves according to the situation after making an inference about the injuries sustained by the victims and the damage to the vehicle. In this work, three machine learning models based on Naive Bayes (NB), Gaussian mixture model (GMM), and decision tree (DT) techniques are compared to identify the best ADC model. Five physical parameters related to vehicle movement, i.e., speed, absolute linear acceleration (ALA), change-in-altitude, pitch, and roll, have been used to train and test each candidate ADC model to identify the correct class of accident among collision, rollover, falloff, and no accident. NB-based ADC model is found to be highly accurate with 0.95 mean F1-score.read more
Citations
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A Comprehensive Review of Indoor/Outdoor Localization Solutions in IoT era: Research Challenges and Future Perspectives
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A Privacy-Preserving Identity Authentication Scheme Based on the Blockchain
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I and i
TL;DR: There is, I think, something ethereal about i —the square root of minus one, which seems an odd beast at that time—an intruder hovering on the edge of reality.
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Middleware for Internet of Things: A Survey
TL;DR: This paper outlines a set of requirements for IoT middleware, and presents a comprehensive review of the existing middleware solutions against those requirements, and open research issues, challenges, and future research directions are highlighted.
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The golden hour: scientific fact or medical "urban legend"?
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TL;DR: A detailed literature and historical record search for support of the "golden hour" concept is discussed, finding none is identified.
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A deep learning approach for detecting traffic accidents from social media data
TL;DR: This paper thoroughly investigates the 1-year over 3 million tweet contents in two metropolitan areas: Northern Virginia and New York City and shows that paired tokens can capture the association rules inherent in the accident-related tweets and increase the accuracy of the traffic accident detection.
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The probability of death in road traffic accidents. How important is a quick medical response
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