Fall Detection System in the Elderly using IoT and AI

Abstract: Artificial intelligence and deep learning methods are used in the suggested fall detection system for senior persons in order to reliably recognize falls in real-time. In contrast to conventional systems, which rely on Internet of Things (IoT) gadgets, this system uses wearable gadgets and sensors to collect data, which is then analyzed using AI algorithms. The device can tell the difference between falls and other movements with great accuracy, alerting caregivers or emergency personnel as needed. The system can continuously learn and increase its accuracy over time thanks to the application of AI and deep learning, which ensures accurate fall detection for senior people. As falls are a primary cause of injury and death in the aged population, fall detection systems are becoming more and more crucial. In order to detect falls and notify caretakers or emergency services, traditional fall detection systems rely on Internet of Things (IoT) devices, such as wearable sensors or smart home technologies. These systems, however, can be expensive and might not be available to everyone. In this study, we suggest an IoT-free fall detection system for older people that makes use of artificial intelligence (AI) and deep learning techniques. Our technology uses information from furniture found in most homes, including chairs, tables, and bed frames, to identify falls and notify caretakers. Our system accurately distinguishes falls from other movements and ascertains the fall severity using machine learning techniques.

Keywords: Fall detection system in aged population, IoT, Convolutional Neural Network model, Convolution Neural Network (CNN) architecture, You only look once version (YOLO), MobileNet, ResUNet and DeepUNet.