Real-time accident detection and physiological signal monitoring to enhance motorbike safety and emergency response

• URL: http://arxiv.org/abs/2403.19085v1
• Date: Thu, 28 Mar 2024 01:41:31 GMT
• Title: Real-time accident detection and physiological signal monitoring to enhance motorbike safety and emergency response
• Authors: S. M. Kayser Mehbub Siam, Khadiza Islam Sumaiya, Md Rakib Al-Amin, Tamim Hasan Turjo, Ahsanul Islam, A. H. M. A. Rahim, Md Rakibul Hasan,
• Abstract summary: Rapid urbanization and improved living standards have led to a substantial increase in the number of vehicles on the road. Motorbike accidents pose a particularly high risk, often resulting in serious injuries or deaths. We propose a novel automatic detection and notification system specifically designed for motorbike accidents.
• Score: 0.35337216626844875
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Rapid urbanization and improved living standards have led to a substantial increase in the number of vehicles on the road, consequently resulting in a rise in the frequency of accidents. Among these accidents, motorbike accidents pose a particularly high risk, often resulting in serious injuries or deaths. A significant number of these fatalities occur due to delayed or inadequate medical attention. To this end, we propose a novel automatic detection and notification system specifically designed for motorbike accidents. The proposed system comprises two key components: a detection system and a physiological signal monitoring system. The detection system is integrated into the helmet and consists of a microcontroller, accelerometer, GPS, GSM, and Wi-Fi modules. The physio-monitoring system incorporates a sensor for monitoring pulse rate and SpO$_{2}$ saturation. All collected data are presented on an LCD display and wirelessly transmitted to the detection system through the microcontroller of the physiological signal monitoring system. If the accelerometer readings consistently deviate from the specified threshold decided through extensive experimentation, the system identifies the event as an accident and transmits the victim's information -- including the GPS location, pulse rate, and SpO$_{2}$ saturation rate -- to the designated emergency contacts. Preliminary results demonstrate the efficacy of the proposed system in accurately detecting motorbike accidents and promptly alerting emergency contacts. We firmly believe that the proposed system has the potential to significantly mitigate the risks associated with motorbike accidents and save lives.

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