IoT-Enabled Smart Helmet for Real-Time Alcohol Monitoring, Accident Detection, and Emergency Alert Communication Using ESP32

IoT-Enabled Smart Helmet for Real-Time Alcohol Monitoring, Accident Detection, and Emergency Alert Communication Using ESP32 1Abhinaba Patra, 1Akash Kumar Bag, 1Banashree Gayen, 1Bhabatosh Mahato, 1Chiranjeeb Roy Chowdhury, 2Dibyendu Chowdhury, 2Chanchal Kumar De 1UG Student, Department of Electronics & Communication Engineering, Haldia Institute of Technology, Haldia, Purba Medinipur, West Bengal 2Department of Electronics & Communication Engineering, Haldia Institute of Technology, Haldia, Purba Medinipur, West Bengal ABSTRACT Road traffic crashes remain among the leading causes of death and disability worldwide, and riders of two-wheelers are disproportionately represented among the fatalities because a conventional helmet offers only passive impact protection and no capability for monitoring, prevention, or post-crash response. This paper presents the design, implementation, and evaluation of a wearable, ESP32-based smart-helmet prototype that combines four safety functions within a single low-cost platform: breath-alcohol screening using an MQ-3 gas sensor, real-time fall/impact detection using a six-axis MPU6050 accelerometer-gyroscope, autonomous location acquisition through a NEO-6M GPS receiver, and automated emergency notification through a SIM800L GSM module, supplemented by Bluetooth-based rider vital-sign monitoring. A threshold-based sensor-fusion routine running on the microcontroller distinguishes normal riding dynamics from unsafe or accident conditions and triggers a coordinated alert sequence — buzzer activation, GPS coordinate acquisition, and SMS/voice dispatch to pre-registered emergency contacts — without requiring rider intervention. Bench and field trials confirmed reliable threshold discrimination for both the alcohol and impact-detection subsystems and consistent, low-latency delivery of location-tagged emergency messages. The prototype demonstrates that an integrated preventive-and-reactive safety layer can be retrofitted onto an ordinary helmet at low cost, and the paper concludes with directions for structural, connectivity, and AI-based enhancements that could be pursued in future iterations.