Wearable Smart Helmet with IoT Connectivity and Machine Learning for Real-Time Safety Monitoring and Alerts

Road accidents, especially involving two-wheelers, pose a significant risk to rider safety. To address this issue, this project proposes a wearable smart helmet with iot connectivity and machine learning for real-time safety monitoring and alerts. The system integrates alcohol detection, a helmet usage verification switch, and an NRF transmitter in the helmet, while the bike unit is equipped with an NRF receiver, motor control, buzzer, LCD display, and arduino for real-time monitoring and response. o The smart helmet ensures that the rider is wearing it properly and is not under the influence of alcohol before allowing the bike to start. If alcohol is detected or the helmet is not worn, the system prevents ignition, ensuring enhanced safety. Additionally, the ADXL345 accelerometer sensor detects accidents or falls, triggering an emergency response. In the event of an accident, the system automatically sends an alert SMS with GPS location coordinates using a GSM module, enabling faster rescue operations. o To further improve safety, the project incorporates machine learning on raspberry pi to predict accident severity based on sensor values, allowing authorities to prioritize emergency responses based on the intensity of the crash. The iot integration ensures remote monitoring, real-time data logging, and accident trend analysis for future safety improvements. o This innovative wearable smart helmet system provides a comprehensive and proactive approach to rider safety, integrating preventive measures, real-time accident detection, and AI-based severity prediction to significantly reduce risks and enhance road safety.