International Journal of Technology and Applied Science

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Volume 17 Issue 4
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Helmet Sense IOT, AI-Driven Rider Safety and Risk Alert System

Author(s) B. Balakrishna, M. Keerthana, K. Sri Vaidhika, K. Sindhu, V. Keerthi Priya
Country India
Abstract Motorcycle accidents remain a significant cause of traffic-related fatalities worldwide, often resulting from rider negligence, impaired driving, and inadequate safety measures. This paper presents a novel intelligent motorcycle safety system that integrates Internet of Things (IoT) sensors, machine learning algorithms, and real-time monitoring to enhance rider safety. The proposed system implements pre-ignition safety checks through helmet detection and alcohol level sensing, ensuring the motorcycle ignition activates only when both conditions are satisfied. Post-ignition, the system continuously monitors riding parameters including speed, weather conditions, and road type to predict accident risk using a machine learning model. The system features a comprehensive web-based dashboard that displays numerical risk scores, visual LED indicators (green for safe, red for high risk, yellow/orange for moderate risk), and voice alerts to provide immediate feedback to riders. Additionally, the system incorporates data analytics and ride history tracking capabilities for longitudinal safety assessment. This multi-layered approach addresses critical safety gaps in motorcycle transportation by combining preventive measures with predictive analytics, contributing to the broader goals of smart city development and sustainable transportation systems.
Field Engineering
Published In Volume 17, Issue 4, April 2026
Published On 2026-04-12
Cite This Helmet Sense IOT, AI-Driven Rider Safety and Risk Alert System - B. Balakrishna, M. Keerthana, K. Sri Vaidhika, K. Sindhu, V. Keerthi Priya - IJTAS Volume 17, Issue 4, April 2026.
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