Real-Time Elderly Fall Detection as Ambient Intelligence in Old Age Homes
Laxmi Shabadi, M Chaitra, L Monik, S G Pavan, R K Deekshith, Laxmi Narayan · 2025
The rapid expansion of autonomous technologies, the rise of computer vision, and edge computing present exciting opportunities in healthcare monitoring systems. Fall prevention is especially important for the elderly because falls from this age group often result in fatalities and serious injuries. Fall detection devices that can quickly recognize falls and alert emergency services have become more and more popular as a result. The primary goal of the project is to increase elderly home safety by implementing an ambient intelligence-based automated emergency recognition system. We present a unique method for fall posture detection utilizing an intelligence surveillance camera and a class of efficient models called MobileNets for mobile and embedded vision applications. A real-time embedded solution persuaded from “edge AI” is implemented using the state-of-the-art Object detection: single shot detector (SSD) Mobile Net V2 and Internet of things embedded GPU platform NVIDIA’s Jetson Nano.