Abstract:
Accidents pose a significant threat all over the world, which often results in severe harm and
loss. Existing solutions are primarily aimed at vehicle-related accidents and are predominantly
based on smartphones, thus leaving a loophole to detect and alert accidents. This research
proposes an IoT and machine learning-based personalized human accident detection and
tracing system that aims to address this gap. The platform consists of three key components: (1)
an IoT enabled smart band with sensors to monitor real-time vital signs heart rate, blood
pressure, body temperature and SpO₂ supported by GPS for precise location tracking; (2) a user
specific machine learning model that identifies abnormal states of health based on personal
physiological patterns; and (3) a cross-platform mobile application that provides real-time
emergency alerts and location information to respective responders. The readings from the
sensor are transmitted via Wi-Fi to a cloud server to reduce dependency on the victim's
smartphone and reduce latency as compared to GSM/GPRS-based systems. The machine
learning model was trained using publicly accessible datasets as well as locally collected data
and it had an accuracy of 99.44% using a Random Forest classifier. Comparative verification
against medical-grade devices resulted in good measurement agreement with Pearson
correlation coefficients of greater than 0.94 for all the parameters. Field trials confirmed stable
device function, accurate sensor operation and compatibility with Android and iOS platforms,
enabling detection outside of automobile contexts. Moreover, provided an extensive locally
obtained physiological data set of significant utility for future accident prevention and targeted
healthcare research. By curtailing detection to response time and raising predictive accuracy,
offers a valuable advance in protecting the public from common accidents.
