Guardian Angel: An Innovative Mobile Application for Rapid Accident Notification and Emergency Response
Keywords:
Accident, Alert, Emergency, GPS, Mobile applicationAbstract
The conceptual design and consequences of a groundbreaking mobile application that is intended to drastically improve reaction times and outcomes following road accidents drastically are discussed in this study. Taking into account that injuries and fatalities might be gravely exacerbated by delayed intervention, this application takes advantage of the smartphone technology that is easily available in order to provide emergency services and pre-designated contacts with information that is immediate, exact, and crucial. The user has the option of manually triggering notifications, but the primary role of the system is to automatically detect accidents by utilizing the sensors (accelerometer, gyroscope, and GPS) that are included in smartphones. These sensors are used to identify sudden impacts or changes in motion that are suggestive of a crash. The application is designed to immediately transmit essential data, such as the user’s identity, the precise GPS location, pre-configured medical information (including allergies, conditions, and blood type), and details about who should be contacted in the event of an emergency. This information will be sent directly to personal emergency contacts as well as to emergency dispatch centers (for instance, 911 or 112) as soon as the application detects that an emergency is occurring. The goal of this system is to circumvent the often-delayed human element of accident reporting, giving a proactive solution that increases the survival rates of victims, reduces the number of serious injuries, and improves the allocation of essential emergency resources.
References
H. Zhang, R. Zhang, and J. Sun, “Developing real-time IoT-based public safety alert and emergency response systems,” Sci. Rep., vol. 15, p. 29056, 2025. Available: https://www.nature.com/articles/s41598-025-13465-7
B. Sumathy et al., “Vehicle accident emergency alert system,” IOP Conf. Ser.: Mater. Sci. Eng., vol. 1012, p. 012042, 2021. DOI: 10.1088/1757-899X/1012/1/012042
M. S. Arefin et al., “Real-time rapid accident detection for optimizing road safety in Bangladesh,” Heliyon, vol. 11, no. 4, p. e42432, Feb. 2025. https://doi.org/10.1016/j.heliyon.2025.e42432
V. Gupta, S. Agarwal, P. Rawal, M. Kumar, A. Rana, and S. Garg, “Accident detection and alarm system for emergency help services,” 2023 International Conference on Sustainable Communication Networks and Application (ICSCNA), Theni, India, 2023, pp. 333-341, https://ieeexplore.ieee.org/document/10370372
M. Ramya Devi and S. Lokesh, “Intelligent accident detection system by emergency response and disaster management using vehicular fog computing,” Automatika, vol. 65, no. 1, pp. 117–129, 2023. doi: https://doi.org/10.1080/00051144.2023.2288483
A. M. Bardol and O. S. Desai, “Vehicle accident detection and notification system,” Int. Res. J. Modern. Eng. Technol. Sci., vol. 5, no. 5, May 2023. Available: https://www.irjmets.com/uploadedfiles/paper//issue_5_may_2023/40695/final/fin_irjmets1685410910.pdf
S. Vasireddy and K. S. Pyla, “Real-time accident detection and emergency notification system for mobile devices,” Int. J. Res. Appl. Sci. Eng. Technol. (IJRASET), vol. 13, no. 7, 2025. Available: https://www.ijraset.com/best-journal/realtime-accident-detection-and-emergency-notification-system-for-mobile-devices
A. M. S. V. Sushma, A. A. Priyanka, S. Tata, T. S. Kumar, M. A. Kumar, and B. S. Kandula, “Real-time IoT-based vehicle accident detection and emergency response system using multi-sensor fusion,” 2025 6th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV), Tirunelveli, India, 2025, pp. 1256-1263, doi: https://doi.org/10.1109/ICICV64824.2025.11085941
K. K. S. Liyakat and R. G. Konnur “Vehicle health monitoring system (VHMS) by employing IoT and sensors,” Grenze Int. J. Eng. Technol., vol. 10, no. 2, pp. 5367–5374, 2024. Available: https://thegrenze.com/index.php?display=page&view=journalabstract&absid=3371&id=8
S. Odnala, R. Shanthy, B. Bharathi, C. Pandey, A. Rachapalli, and K. K. S. Liyakat, “Artificial intelligence and cloud-enabled e-vehicle design with wireless sensor integration,” SSRN Electron. J., 2025. Available: https://doi.org/10.2139/ssrn.5107242
K. S. Liyakat, “Modelling and simulation of electric vehicle for performance analysis: BEV and HEV electrical vehicle implementation using Simulink for e-mobility ecosystems,” in E-Mobility in Electrical Energy Systems for Sustainability, L. D., N. Nagpal, N. Kassarwani, V. Varthanan G., and P. Siano, Eds. IGI Global, 2024, pp. 295–320. doi: https://doi.org/10.4018/979-8-3693-2611-4.ch014
K. S. Liyakat, “Role of carbon-based supercapacitors in regenerative braking for electrical vehicles,” in Innovations in Next-Generation Energy Storage Solutions, M. Mhadhbi, Ed. IGI Global Scientific Publishing, 2025, pp. 523–572. Available: https://doi.org/10.4018/979-8-3693-9316-1.ch017
S. Liyakat, “Computer-aided diagnosis in ophthalmology: A technical review of deep learning applications,” in Transformative Approaches to Patient Literacy and Healthcare Innovation, M. Garcia and R. de Almeida, Eds. IGI Global, 2024, pp. 112–135. Available at: https://www.igi-global.com/chapter/computer-aided-diagnosis-in-ophthalmology/342823
S. Liyakat, “Machine learning-driven internet of medical things (ML-IoMT)-based healthcare monitoring system,” in Responsible AI for Digital Health and Medical Analytics, B. Soufiene and C. Chakraborty, Eds. IGI Global Scientific Publishing, 2025, pp. 49–86. Available: https://doi.org/10.4018/979-8-3693-6294-5.ch003
A. N. Upadhyaya, C. Surekha, P. Malathi, G. Suresh, K. Suriyan, and K. K. S. Liyakat, “Pioneering cognitive computing for transformative healthcare innovations,” SSRN Electron. J., 2025. doi: https://doi.org/10.2139/ssrn.5086894
B. Parihar, K. Ajmeera, S. Valaboju, S. Z. Rashid, and A. S. L. D. R., “Enhancing data security in distributed systems using homomorphic encryption and secure computation techniques,” ITM Web Conf., vol. 76, p. 02010, 2025. doi: https://doi.org/10.1051/itmconf/20257602010
C. Veena, M. Sridevi, K. K. S. Liyakat, B. Saha, S. R. Reddy, and N. Shirisha, “HEECCNB: An efficient IoT-cloud architecture for secure patient data transmission and accurate disease prediction in healthcare systems,” in 2023 Seventh Int. Conf. Image Inf. Process. (ICIIP), Solan, India, 2023, pp. 407–410. Available: https://ieeexplore.ieee.org/document/10537627
K. S. Liyakat, “AI-driven-IoT (AIIoT)-based decision making in kidney diseases patient healthcare monitoring: KSK approach for kidney monitoring,” in AI-Driven Innovation in Healthcare Data Analytics, L. Ö. Polat and O. Polat, Eds. IGI Global Scientific Publishing, 2025, pp. 277–306. doi: https://doi.org/10.4018/979-8-3693-7277-7.ch009
M. A. Mahant, “Machine learning-driven Internet of Things (MLIoT)-based healthcare monitoring system,” in Digitalization and the Transformation of the Healthcare Sector, N. Wickramasinghe, Ed. IGI Global Scientific Publishing, 2025, pp. 205–236. doi: https://doi.org/10.4018/979-8-3693-9641-4.ch007
P. M. Nerkar and B. U. Dhaware, “Predictive data analytics framework based on heart healthcare system (HHS) using machine learning,” J. Adv. Zool., vol. 44, Special Issue 2, pp. 3673–3686, 2023. Available: https://jazindia.com/index.php/jaz/article/view/1695
S. B. Khadake, A. B. Chounde, A. A. Suryagan, M. H. M., and M. R. Khadatare, “AI-driven-IoT (AIIoT) based decision making system for high-blood-pressure patient healthcare monitoring,” in 2024 Int. Conf. Sustainable Commun. Netw. Appl. (ICSCNA), Theni, India, 2024, pp. 96–102. Available: https://ieeexplore.ieee.org/document/10863954
S. Sayyad, “Healthcare monitoring system driven by machine learning and Internet of Medical Things (MLIoMT),” in Convergence of Internet of Medical Things (IoMT) and Generative AI, V. Kumar, P. Katina, and J. Zhao, Eds. IGI Global Scientific Publishing, 2025, pp. 385–416. Available: https://doi.org/10.4018/979-8-3693-6180-1.ch016