iHELM:An IoT-Based Smart Helmet for Real-time Motorbike Accident Detection and Emergency Healthcare Services

Road accidents are a cause of significant cause of death and morbidity around the globe. In extreme cases, they often result in property damage, severe injury, and even death. For bikers, accidents are a more significant concern. Lack of enough safety measures, poor roads, and faulty helmets result in a high probability of disaster. Many unfortunate individuals lose their life or suffer severe trauma due to a lack of immediate medical attention. Many solutions exist to solve this issue. Some use a combination of sensors to observe every aspect of the rider, from accident detection to his health condition. These solutions don't incorporate IoT for real-time monitoring of the rider, which can be pretty efficient for notifying healthcare organizations. The proper handling of false positives generated during data collection becomes necessary. To handle these issues and provide a respite to the bikers, iHELM introduces a practical model that combines IoT and machine learning to solve this issue. iHELM detects the accident case scenarios, filters out the false positives, and finally informs the authorities required about the rider's condition. Testing our model predicted severe accidents with an accuracy of 90 %, and our model sent an SOS with a time delay of fewer than 15 minutes.