Privacy-aware IoT Fall Detection Services For Aging in Place

--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 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. The Internet of Things (IoT) enables everyday physical objects, or "things," to be connected to the Internet [1]. These objects are often equipped with pervasive intelligence capabilities. IoT devices' capabilities may be abstracted as IoT services [2]. An IoT service has a set of functional and non-functional, i.e., quality of service (QoS) properties.