Smart Fall Detection: AI-Powered Airbag Deployment for Injury Prevention
Bindu Madavi, Krishna Sowjanya K · 2025
Falls among elderly individuals are a leading contributor to injury and death worldwide. As the aging population continues to rise, there will be an even greater demand for advanced safety solutions to minimize the risk of falls. Therefore, this research presents a Human Fall Detection and Airbag Implementation System designed to provide real-time fall detection and injury prevention. The operation of the system takes place in two phases: first, fall detection, and second, alert generation. In the first phase, the subjects use a smart wearable device with a gyroscope and accelerometers to constantly monitor movement and orientation. The sensor data thus collected is preprocessed and then analyzed using signal processing techniques to distinguish normal activities from a possible fall event. The motion pattern is fed into a machine learning model-i.e., a Random Forest algorithm-that assesses in real-time whether an event is likely to be a fall. In case of a high-confidence fall detection, immediate airbag deployment is triggered to minimize the impact and help protect critical body parts like the head, back, and hips. As for the second phase, the system automatically alerts caregivers and nearby medical services for immediate assistance if no movement occurs, even after a threshold time limit. The AI-based technology not only boosts fall detection accuracy but also reduces false alarms, thus providing a dependable solution for aged individuals, impaired patients, and even workers in hazard-prone environments. The proposed system thus aims to provide a better quality of life for these at-risk individuals, allowing higher levels of independence while preventing and/or reducing the risk of severe fall injuries.