A Machine Learning-Based Smart Helmet System for Reliable Two-Wheeler Accident Detection and False Alarm Reduction

Abstract

Accidents involving two-wheelers are a major cause of injury and death around the world. One of the major problems associated with the management of two-wheeler accidents is the time delay associated with the detection of the accident and providing instant support to the victim. In the existing system, the detection of the accident is done by simple sensors, leading to false alarms due to hard braking, bumps, and dropping of the helmet by the rider. In order to overcome the problems associated with the existing system, a machine learning-based intelligent helmet system has been proposed in the following paragraphs. In the intelligent system, the accident is detected by the acceleration and gyroscope sensors, which are used to detect the patterns and forces experienced by the rider during the accident. The machine learning algorithm recognizes the pattern experienced by the rider during a two-wheeler accident, thereby identifying the difference between the normal pattern and the accident experienced by the rider. The intelligent system will send a message to the emergency contacts if a real accident is identified by the machine learning algorithm, thereby providing instant support to the victim. The implementation of the intelligent system will provide safety to the rider.