Advanced Smart Helmet

Abstract

Owing to the lack of real-time monitoring and the resultant delay in detection, crutial industrial and road accidents cause major and serious injuries. To improve the safety of bike riders, and industrial workers this paper gives best solution that combines IoT, embedded machine learning and multimodal sensing. This system usesgas sensors for detection, heart rate and SpO2 sensors for real health monitoring, IMU sensor for fall detection and GPS for real time location. Compared to cloud-based solutions , the proposed helmet anomaly detection capability on the hardware itself improves the reliability of the system and helps to reduce latency by utilizing TinyML-based edge intelligence. In an IoT –enabled environment this system increase accuracy, reaction time and safety of bike riders and industrial workers. With some technological developments, this paper proposes an AI-Supported Edge-Intelligent Smart Helmet that combines multimodal sensing, secure communication channels, TinyML edge intelligence and scalable IIoT infrastructure. The proposed system combines the functionalities of fall detection, physiological sensing, environmental harmful sensing and grolocation tracking into a single platform. The proposed system capitalizes on the advantages of running lightweight machine learning models on the devide to overcome communication latency, bandwidth and functionality even in conditions of poor network connectivity. Secure IoT communication channels facilitate efficient emergency notification systems, while cloud connectivity facilitates historical data analysis and system-wide optimization.

Introduction

This work presents a smart helmet system based on IoT, edge computing, and embedded sensing technologies designed to improve road and industrial safety. Traditional helmets provide only passive protection, while modern safety requirements demand real-time monitoring, accident detection, and emergency response capabilities. IoT-enabled smart helmets address these needs by integrating sensors, communication modules, and intelligent processing systems.

The literature review shows the evolution of smart helmets from simple accident detection systems using accelerometers and GPS to more advanced solutions incorporating cloud computing, alcohol detection, multimodal sensing, and edge AI (TinyML). Recent research emphasizes the importance of combining physiological, environmental, and motion data to improve detection accuracy. However, challenges remain in reducing latency, ensuring privacy, and enabling real-time decision-making without relying heavily on cloud systems.

The proposed system introduces a three-layer architecture consisting of:

• Sensing Layer: MQ-3 alcohol sensor, helmet detection sensor, and eye-blink (drowsiness) detection sensor
• Edge Processing Layer: Arduino-based real-time decision-making for classifying rider status as SAFE or UNSAFE
• Communication & Actuation Layer: 433 MHz RF module for transmitting safety status and controlling bike ignition via relay system

The system continuously monitors rider condition, including alcohol level, helmet usage, and drowsiness, and only allows ignition when all conditions are safe. If any unsafe condition is detected, the engine remains locked and alerts are triggered through buzzer and LCD display. Communication between helmet and bike is fast and low-latency, ensuring real-time response.

Conclusion

This project proposes an Arduino-based smart helmet system that effectively addresses the needs of two-wheeler riders to increase their safety. The system integrates helmet wear detection, alcohol sensing, accident detection, and real-time emergency alert transmission in compact form, thereby facilitating accident avoidance and rapid response in the case of an emergency. The experimental results assure the system\'s efficiency in preventing vehicle ignition under unsafe conditions and sending an alert message with the exact location in the case of an accident. This proposed design is affordable, simple to implement, and suitable for real-world applications. Such a smart helmet system, when packaged with further improvement features, such as integration with a mobile app, cloud connectivity, and better power management, can be highly effective for large-scale implementations and can contribute considerably to road safety.

References

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Copyright

Copyright © 2026 Prerana A. Pasare, Saurabh S. Pawar, Ved S. Patankar, Milind Patil. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.