Design & Implementation of IoT Based Smart Helmet for Road Accident Detection

With the growing number of 2-wheel motor vehicles, the frequency of accidents is on the rise. A major portion of the fatalities occur because the person was either not wearing a helmet, or his accident was not reported in time, and he could not be saved because of the delayed admittance to a hospital, or because he was riding while drunk. We propose mechanisms that can detect if one is wearing the helmet, detect accidents, and detect whether the person has over-consumed alcohol. For this purpose, we use onboard sensors – IR Sensor, accelerometer (ADXL355) and breath-analyzer (MQ3). The accelerometer measures the change in tilt, in X Y and Z axes respectively, and sends the data to a server via an online application programming interface (API). The breath analyzer senses the amount of alcohol present in the breath of a person wearing the helmet and reports if it is beyond the legal limit. The server also uses the data gathered from the accelerometer and the IR sensors, to train a support vector machine (SVM). This can help optimize accident detection in the future when enough data is gathered to provide reliable accuracy. This will ensure the holistic safety of the rider at all times.

Keywords: GPRS, GPS, IOT, WSN, ZIGBEE, Arduino uno.