Smart Helmet with Sensor Fusion and Mobile Connectivity

Abstract

The integration of sensor technologies in personal safety devices is transforming how we approach risk management in various high-risk environments. The Smart Helmet with Sensor Fusion and Mobile Connectivity provides an advanced solution for enhancing rider safety by leveraging multiple sensors like accelerometers, gyroscopes, GPS, and communication modules. This helmet detects critical events, such as accidents or falls, using data from the MPU6050 sensor (for motion tracking) and GPS data from the NEO-6M to pinpoint precise locations of incidents. Additionally, it communicates with emergency contacts via the SIM800L module to provide real-time crash notifications. Flutter-based mobile applications enable seamless connectivity between the helmet and user smartphones, offering features such as live data tracking, including speed, acceleration, and distance travelled. This system uses sensor fusion techniques to process real-time data from various sources, ensuring accurate event detection and prompt alerts. This technology is ideal for bikers, construction workers, and others working in high-risk settings, providing both immediate safety responses and long-term health monitoring.

Introduction

Motorcycle rider safety in urban areas is increasingly critical due to frequent accidents from unexpected hazards. Traditional helmets offer passive protection, but smart helmets with integrated sensors and mobile connectivity enable proactive safety by continuously monitoring rider motion and environment.

The developed smart helmet prototype uses sensors such as accelerometers, gyroscopes (MPU6050), GPS (NEO-6M), and GSM communication (SIM800L) managed by an ESP32 microcontroller. It detects accidents or falls in real-time and sends emergency alerts with precise location data to predefined contacts via SMS. A Flutter-based mobile app supports live tracking, alert management, and crash data logging, enhanced by Firebase cloud services for storage and authentication.

Key features include:

• Sensor Fusion: Combines multiple sensors to improve detection accuracy and reduce false alarms.

• Real-Time Alerts: Immediate notification of accidents via GSM communication, enabling faster emergency response.

• User Interface: Mobile app allows monitoring helmet status, viewing location, and managing contacts.

• Robustness: Designed to work in urban, rural, and low-connectivity areas.

• Testing Results: Achieved 92% accuracy in crash detection, with alerts typically sent within seconds under good network conditions.

Challenges include dependency on GSM and GPS signal quality, potential false positives, and battery life (~8 hours). Overall, the system represents a shift from reactive to predictive motorcycle safety, promoting responsible riding and potentially reducing fatalities and medical costs.

Conclusion

The Smart Helmet with Sensor Fusion and Mobile Connectivity delivers a comprehensive safety solution by combining motion detection, GPS tracking, and emergency communication. With a strong focus on accident response and rider awareness, the system not only detects crashes but also transmits real-time alerts and location data to emergency contacts.Built on a foundation of ESP32, MPU6050, SIM800L, and NEO-6M modules, and supported by a user-friendly Flutter mobile application, the system ensures both technological effectiveness and usability. It is particularly beneficial for motorcyclists and workers in high-risk environments.While the system currently requires reliable GSM coverage and battery management for optimal performance, future enhancements could include AI-based crash prediction, solar-powered modules, voice control features, and 5G integration to support faster and more reliable data transmission. These advancements will further improve the helmet\'s real-time capabilities, scalability, and applicability in diverse environments, promoting safer commuting and workplace standards.

References

[1] Agarwal, Nitin, Anshul Kumar Singh, Pushpendra Pratap Singh and Rajesh Sahani. “Smart Helmet.” International Journal of Innovative Technology and Exploring Engineering (2020). [2] Impana, H. C., M. Hamsaveni and H. T. Chethana. “A Review on Smart Helmet for Accident Detection using IOT.” EAI Endorsed Transactions on Internet of Things (2020). [3] Kuhar, Preeti, Kaushal Sharma, Yaman Hooda and Neeraj Kumar Verma. “Internet of Things (IoT) based Smart Helmet for Construction.” Journal of Physics: Conference Series 1950 (2021). [4] Mohd Rasli, Mohd Khairul Afiq, N. K. Madzhi and Juliana Johari. “Smart helmet with sensors for accident prevention.” 2013 International Conference on Electrical, Electronics and System Engineering (ICEESE) (2013): 21-26. [5] Priya C, Ramya. C, Poovitha N. M, Rithika P. S, Sujithra. M, \"Smart Bike Helmet with Vehicle Tracking System using Arduino\", 2022 International Conference on Edge Computing and Applications (ICECAA), pp.579-582, 2022. [6] Rajesh Kumar Sharma, Gaurav Kumar, Bestley Joe S, \"Smart Helmet Prototype for Safety Riding and Alcohol Detection\", 2020 IEEE Bangalore Humanitarian Technology Conference (B-HTC), pp.1-5, 2020. [7] Ramanathan Subramaniyan, Saravanak Kumuran, Kathirvelan J, \"Smart Helmet System with Wireless Communication through GSM\", 2024 3rd International Conference on Artificial Intelligence for Internet of Things (AIIoT), pp.1-6, 2024. [8] K.Sai Sampath, Belvin Benny, K.Avinash, M.Rama krishna, \"SMART SAFETY HELMET\", IJRAR - International Journal of Research and Analytical Reviews (IJRAR), E-ISSN 2348-1269, P- ISSN 2349-5138, Volume.6, Issue 1, Page No pp.156 159, March 2019. [9] Sheetalrani Rukmaji Kawale, Shruti Mallikarjun, Dankan Gowda V, KDV Prasad, Shekhar R, Anil Kumar N, \"Design and Implementation of an AI and IoT-Enabled Smart Safety Helmet for Real-Time Environmental and Health Monitoring\", 2024 IEEE International Conference on Information Technology, Electronics and Intelligent Communication Systems (ICITEICS), pp.1-7, 2024. [10] Shravya, K. S., Yamini Mandapati, Donuru Keerthi, K Harika and Ranjan Kumar Senapati. “Smart helmet for safe driving.” E3S Web of Conferences (2019). [11] S. Umamaheswari, Deepak Kumar, Anurag Roy, Aayushmaan Shrivastava, \"Intelligent Headgear System\", 2024 3rd International Conference on Applied Artificial Intelligence and Computing (ICAAIC), pp.1856-1862, 2024. 2023

Copyright

Copyright © 2025 Dr. John Prakash Veigas, Saurav , Shetty Gaurav Gopalakrishna, Nirikshith P Shetty , Sunilkumar Muragesh Dabbanad. 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.