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@article{200454,
        author = {Janav N. Parsankar and Kshitij S. Wankhede and Nikhil K. Payghan and Harshal K. Kubade and Pratik R. Kharate and Rutik A. Dhage and Sourabh C. Hingne},
        title = {Smart Helmet},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {1924-1930},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=200454},
        abstract = {Road safety has become a critical global concern, particularly in developing countries where two-wheeler usage is significantly high and accident rates continue to rise due to human negligence. Major contributing factors include failure to wear helmets, driving under the influence of alcohol, and reduced alertness caused by fatigue or drowsiness. This paper proposes a Smart Helmet system designed to address these challenges through the integration of embedded systems, sensor technologies, and wireless communication.
The proposed system consists of a dual-module architecture: a helmet-mounted transmitter unit and a vehicle-mounted receiver unit. The helmet unit is equipped with infrared (IR) sensors for detecting helmet usage and monitoring eye-blink patterns to identify drowsiness, along with an MQ-3 gas sensor for detecting alcohol concentration in the rider’s breath. These sensors continuously collect real-time data, which is processed using an Arduino Uno microcontroller. The processed information is then transmitted wirelessly to the receiver module via Radio Frequency (RF) communication.
The receiver unit, also based on an Arduino microcontroller, interprets the incoming data and makes decisions regarding vehicle ignition control. A relay mechanism is employed to either enable or disable the ignition system depending on the rider’s safety status. Additionally, a 16×2 LCD display and buzzer are used to provide real-time feedback and alerts to the rider in case of unsafe conditions.
Experimental evaluation of the system demonstrates reliable performance in detecting helmet usage, alcohol presence, and drowsiness, with prompt response in preventing ignition under unsafe conditions. The system effectively enforces safety compliance before and during vehicle operation, thereby reducing the likelihood of accidents. Furthermore, the use of low-cost components makes the solution economically viable for large-scale adoption.
The proposed Smart Helmet system highlights the potential of combining embedded systems and automation to enhance road safety. Future enhancements may include the integration of Internet of Things (IoT) technologies, GPS-based tracking, GSM communication for emergency alerts, and advanced machine learning techniques for improved drowsiness detection and predictive safety analysis.},
        keywords = {},
        month = {May},
        }