Privacy-aware IoT Fall Detection Services For Aging in Place
This work addresses fall detection for the aging population, enabling safer independent living, but it is incremental as it builds on existing IoT and generative methods.
The paper tackles the problem of fall detection for the elderly by proposing an IoT-based service framework that uses UWB radar sensors and a generative model to address data scarcity and privacy concerns, achieving 90.72% accuracy and 89.33% precision in distinguishing falls from daily activities.
Fall detection is critical to support the growing elderly population, projected to reach 2.1 billion by 2050. However, existing methods often face data scarcity challenges or compromise privacy. We propose a novel IoT-based Fall Detection as a Service (FDaaS) framework to assist the elderly in living independently and safely by accurately detecting falls. We design a service-oriented architecture that leverages Ultra-wideband (UWB) radar sensors as an IoT health-sensing service, ensuring privacy and minimal intrusion. We address the challenges of data scarcity by utilizing a Fall Detection Generative Pre-trained Transformer (FD-GPT) that uses augmentation techniques. We developed a protocol to collect a comprehensive dataset of the elderly daily activities and fall events. This resulted in a real dataset that carefully mimics the elderly's routine. We rigorously evaluate and compare various models using this dataset. Experimental results show our approach achieves 90.72% accuracy and 89.33% precision in distinguishing between fall events and regular activities of daily living.