Authors: Aware Uday Navanath, Prof. S. K. Said, Prof. M. C. Kolse
Abstract: Two-wheeler riders account for a large share of road fatalities in India, and many deaths occur not at the moment of impact but during the delay before help arrives. A conventional helmet offers passive head protection but cannot recognize a crash, assess whether the rider is fit to ride or summon assistance. This paper reviews the design of AI-powered smart helmets that integrate Internet of Things (IoT) sensing, embedded intelligence and wireless communication to close this gap. The review covers alcohol detection with gas sensors, drowsiness detection from eye-blink and physiological signals, helmet-wear verification, and crash detection using inertial and impact sensors. It examines how threshold rules, classical machine learning and lightweight neural networks are used to separate real accidents from everyday shocks such as potholes or dropping the helmet, and how GPS and GSM or smartphone links deliver location-tagged alerts to emergency contacts. A layered architecture covering sensing, edge processing, connectivity, cloud services and user applications is proposed, followed by a research gap analysis, objectives, a comparison of detection approaches and a discussion of power, false-alarm, privacy and cost constraints. The review concludes that a low-cost helmet combining preventive monitoring with reliable post-crash alerting can measurably shorten emergency response times for two-wheeler riders.
DOI: https://doi.org/10.5281/zenodo.24036670
