Trauma accident detecting and reporting system

Fanca, Alexandra; Puscasiu, Adela; Folea, Silviu; Vălean, Honoriu

doi:10.1109/aqtr.2018.8402718

Cited by 10 publications

(9 citation statements)

References 7 publications

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“…We have tried our hardest to make a smart system that is as accurate as possible in identifying accidents and has the shortest possible latency in detecting the approximate position and sending the support text. The paper that was the most similar to ours was A. Fanca et al's work [13], F. Tabei et al's work [14], S. N et al's work [15] F. Bhatti et al's application [16], J. Chang et al's system [17], Yong-Kul Ki et al's paper [18] and M. Fogue et al [19]. In many ways, our work has accomplished far preferable to what they did.…”

Section: Performance Evaluationsupporting

confidence: 77%

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A Smart System to Detect Bike Accidents and Reduce Casualties

Ridwan,

Mary,

Rana

et al. 2024

Preprint

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In this world people are a victim of traffic accidents so easily. It turns out more crucial when they cannot reach out for help. Overcrowdedness and ignorance of traffic rules and regulations are the main reasons for such accidents. The number of bikes in Dhaka has expanded dramatically as a result of the increased popularity of ride-sharing apps such as Pathao, Uber Moto, Shohoz, and others. As a result, bike accidents have become an everyday phenomenon. We have felt the need for an improved bike safety system that ensures bikers' proper emergency services. In this paper, we have proposed a system that constantly detects bike accidents using the MPU6050, SIM808, Raspberry Pi, and Arduino Uno. We also have used a USB camera to record the previous 30 seconds of the accident to identify the reason behind this misfortune. Furthermore, we have used a database to locate the nearest hospital for assistance. If an accident occurs, the sensors will trigger, capture the video information of why the accident has occurred, and send a message containing the number and location of the biker to the nearest hospitals, police stations, and emergency contacts.

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Section: Performance Evaluationsupporting

confidence: 77%

“…With the help of an accelerometer and gyroscope, it can determine high acceleration, axes, and rotational movements, and this helps to eliminate the erroneous detection of a fall. A magnetometer is used to detect the orientation of the smartphone with respect to the position of the body [13].…”

Section: Related Workmentioning

confidence: 99%

A Smart System to Detect Bike Accidents and Reduce Casualties

Ridwan,

Mary,

Rana

et al. 2024

Preprint

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“…Software-based ACN systems, exploiting the versatility of mobile devices, were eventually developed ([ 2 , 3 , 4 , 5 , 6 ], as commercial apps PODIS, Collision Call, SOSmart, DriveSense, Blinkapp, ROADSAVE, SafeBeep). However, both hardware- and software-based systems have a major caveat: they send notifications after the accident.…”

Section: Problem Description and Paper’s Contributionsmentioning

confidence: 99%

“…His method initiates a communication protocol during or after the end of the crash, whereas we actually predict the onset of a number of emergencies and proactively alert the driver or a public safety answering point. Fanca et al [ 2 ] sought an effective, double-pronged approach intended to reduce the number of falls-related deaths by building a system, using mobile phones, to detect and report accidents, when they occur, and reduce the time of accident report and medical intervention to the scene of the accident. Zualkernan et al [ 3 ] created a mobile phone application which uses an artificial intelligence-based classifier software to distinguish between various types of accidents, categorized from a collection of data, derived from simulating different kinds of collisions.…”

Section: Introductionmentioning

confidence: 99%

“…Again, Motion Shield addresses potential emergencies well before they arrive and acts accordingly, whereas [ 2 , 3 , 4 , 5 , 6 ] always alert EMS after the accident, without proposing any surefire way to safeguard the mobile phone from being destroyed during the accident. Alvi et al [ 7 ] presented a critical analysis of major existing technologies associated with accident prediction, detection, and prevention, such as vehicular ad hoc networks, GPS/GSM-based systems, machine learning techniques, etc.…”

Section: Introductionmentioning

confidence: 99%

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Motion Shield: An Automatic Notifications System for Vehicular Communications

Balios¹,

Kyriakidis²,

Zimeras

et al. 2022

Sensors

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Motion Shield is an automatic crash notification system that uses a mobile phone to generate automatic alerts related to the safety of a user when the user is boarding a means of transportation. The objective of Motion Shield is to improve road safety by considering a moving vehicle’s risk, estimating the probability of an emergency, and assessing the likelihood of an accident. The system, using multiple sources of external information, the mobile phone sensors’ readings, geolocated information, weather data, and historical evidence of traffic accidents, processes a plethora of parameters in order to predict the onset of an accident and act preventively. All the collected data are forwarded into a decision support system which dynamically calculates the mobility risk and driving behavior aspects in order to proactively send personalized notifications and alerts to the user and a public safety answering point (PSAP) (112).

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