Jul 2026· International Journal of Drug Delivery Technology· Vol 16· 0 citations· 10 references
TL;DR
This research introduces an intelligent smart accident detection and instant alert generation system based on an embedded system and wireless communication technologies to enable real-time monitoring of vehicles and automated emergency responses.
Abstract
Road accidents are a leading cause of injury and death around the world due to the delayed emergency response and drunk
driving, without the availability of real-time monitoring systems. Traditional accident-reporting procedures rely on
manual communication and lead to longer rescue times and limited prospects of timely medical support. In order to solve
these problems, this research introduces an intelligent smart accident detection and instant alert generation system based
on an embedded system and wireless communication technologies. This proposed solution combines the use of Arduino
Uno, vibration sensors, alcohol sensors, GPS modules, GSM communication, Wi-Fi connectivity, LCD displays, and
buzzer units to enable real-time monitoring of vehicles and automated emergency responses.
The system is constantly assessing the state of the vehicle and driver's actions in real time. The vibration sensor senses
anomaly of the intensity of impact in case of collision and can immediately begin the mechanism for the detection of the
accident. The GPS module gets the exact geographical position of the vehicle and the GSM module automatically sends
out emergency call messages of the accident and the geographical coordinates of the accident to preprogrammed
emergency contacts. The framework is also designed to include an alcohol monitoring feature that detects alcohol
impaired driving conditions, and provides warnings to prevent alcohol impaired driving. Furthermore, Wi-Fi connectivity
allows for cloud-based monitoring and integration with IoT, which can facilitate real-time data analysis and intelligent
transportation solutions.
In the world, road traffic accidents are among the top causes of fatalities: There is a large risk of severe injuries and
fatalities if an emergency response is late. This paper introduces an intelligent road accident detection and emergency alert
system for a smartphone which is based on the multi-sensor data fusion and machine-learning techniques that allow the fast
detection of the accident and early alert notification. The proposed framework uses the data from the vehicle's accelerometer,
gyroscope and Global Positioning System (GPS) to continuously monitor the specific dynamics of the vehicle, recognizing the
abnormal patterns of movement involved in road accidents. The sensor noise is eliminated in a preprocessing step, and
discriminative motion features are extracted from the sensor signals, which are then classified by a Support Vector Machine
(SVM) to discriminate between the collision and normal driving events and minimize false alarms. In the case of a potential
accident being detected, the system activates a reprogrammable confirmation timer the user can use to cancel unintentional
alerts before automatically sending the location of the accident as well as emergency information to preprogrammed contacts.
The proposed method does not require any special in-vehicle hardware, and uses inexpensive sensors from existing
smartphones, which are also widely available, so it is a cost-effective and readily deployable solution. The proposed framework
is evaluated through experimentation, and the results show a high accuracy of detection with a low false-positive rate, while
remaining real-time for practical implementation. Intelligent sensor fusion, machine learning-based classification, and
automated emergency communication contribute to an enhanced road safety, minimizing emergency response time and
improving the reliability of accident detections.
Jaladi Sravanthi, B. Lakshmi· International Journal for Re...· 0 citations
Delay of emergency services and lack of instantaneous reporting of accidents is the main contributor to the serious injuries or death in two-wheeler accidents. A Smart Helmet system has been created to solve this acute problem through intelligent detection of accidents during the accident and automatic notification of emergency services. The helmet has an Inertial Measurement Unit (IMU), an accelerator and a gyroscopic device that constantly reads the movement of the riders and matches any abrupt impact or unusual movement patterns that are signs of a collision. When an accident happens, the system sends the live position of the rider through SMS to a GSM/GPS receiver that triggers an automatic emergency call to predefined contacts or closest police department. The system has a cancel window to avoid false alarms when the bike is suddenly braking or making a minor slip. The rider is free to remain on alert. In addition, it has machine learning (TinyML) that improves the precision of the detection of various motion patterns, including actual collisions, potholes, or regular riding to improve false positives. This use of AI will make sure that there are real accidents that cause the alarm. The Smart Helmet is an IoT-enabled safety solution that was designed as a low-cost solution to safety, guaranteeing not only the speed of medical help but also making roads smarter.
ASHWINI A, N. Nalini, A. Rosi et al.· International Conference on...· 0 citations
The emergence of sensor technologies and embedded devices has created significant potential for autonomous accident-detection systems that will enhance emergency response and road safety. In this paper, an automated accident detection system is proposed that rapidly notifies the emergency center of the accident’s location and severity. Five sensors, such as a gyroscope, an IR flame sensor, a glass-break sensor, a smoke sensor, and a vibration sensor, are integrated with the STM32 microcontroller board. An algorithm is proposed with three severity levels based on the activation of a sensor or group of sensors. Emergency responders can prioritize their interventions by categorizing accidents into severity levels, thereby allocating resources more efficiently and effectively. The proposed method offers significant potential to strengthen road safety and enhance emergency response to road accidents.
Unknown authors· Revue Roumaine des Sciences...· 0 citations
The large number of deaths occur due to alcohol consumption by riders and delayed emergency response. Riders under the influence of alcohol experience a reduced concentration, slow reaction time, and poor decision making ability, increasing the probability of crashes. Additionally, accident victims are often unable to communicate their location due to unconsciousness or severe injuries. This article proposes an Advanced Smart Helmet System for Rider Safety using Internet of Things (IoT) technology. The system integrates an MQ-3 alcohol sensor to detect alcohol through the rider’s breath and measure alcohol percentage in real time. If the alcohol level exceeds a predefined threshold, the system sends an SMS alert containing alcohol percentage details to a registered mobile number. The system also incorporates an accelerometer based crash detection mechanism. When a crash is detected, an emergency buzzer is immediately activated to alert nearby people and draw attention to the accident location. Simultaneously, a GPS module retrieves the real time location and a GSM module sends SMS alerts and initiates emergency calls. If the rider consumed alcohol and an accident occurred, the system sends alerts including alcohol percentage and exact GPS location. Our proposed system enhances rider safety, prevents drunk driving, reduces emergency response time, and provides a reliable and cost effective solution for real world implementation.
Phani Krishna Bulasara, Kuluri Rani, K. Srilakshmi et al.· 2026 7th International Confe...· 0 citations
Floods pose a significant hazard in India, causing severe damage to life,
property, and the economy. Existing flood monitoring systems often suffer from delayed response,
limited coverage, and high costs. The objective of this study is to design and implement a low-cost,
real-time IoT-based smart flood monitoring and early warning system that can improve prediction
accuracy and provide timely alerts to minimize flood impacts
The proposed system integrates multiple sensors-ultrasonic for water level, water flow
sensors, and DHT22 for temperature and humidity-with an Arduino Uno microcontroller. Data is
transmitted to the ThingSpeak cloud platform using the ESP8266 Wi-Fi module and visualized via
the ThingView mobile application. A GSM module sends SMS alerts to authorities and residents
when threshold conditions are detected. The system was simulated using Proteus Professional to
verify performance, and individual modules were tested for accuracy and responsiveness.
The proposed system overcomes limitations of traditional flood monitoring approaches
by enabling automated, continuous, and low-cost sensing with cloud-based data access. Multisensor integration reduces false alarms compared to single-parameter systems. While the Wi-Fi +
GSM approach provides effective coverage for urban and semi-urban areas, rural deployments may
require extended-range communication protocols. Security enhancements and machine learning integration are recommended for predictive analytics and robust performance in div
This IoT-based flood monitoring and early warning system provides a scalable, affordable, and effective solution for real-time flood risk management. By integrating multiple environmental parameters, cloud storage, and multi-channel alerts, it significantly improves upon existing methods. The architecture offers a strong foundation for future enhancements, including AIdriven prediction models and secure data transmission protocols, to further strengthen disaster preparedness and response.
N. Benni, S. S, A. G. et al.· International Journal of Sen...· 0 citations
The design and implementation of an Internet of Things (IoT)-based real-time kitchen monitoring and automation system aimed at enhancing safety, efficiency, and intelligent control within kitchen environments is presented.
Simon Usiju Chagwa, P. B. Zirra· Journal of Analytical and Ap...· 0 citations
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