Privacy-aware IoT Fall Detection Services For Aging in Place

• URL: http://arxiv.org/abs/2506.22462v1
• Date: Wed, 18 Jun 2025 03:28:07 GMT
• Title: Privacy-aware IoT Fall Detection Services For Aging in Place
• Authors: Abdallah Lakhdari, Jiajie Li, Amani Abusafia, Athman Bouguettaya,
• Abstract summary: Fall detection is critical to support the growing elderly population, projected to reach 2.1 billion by 2050.<n>We propose a novel IoT-based Fall Detection as a Service framework to assist the elderly in living independently and safely by accurately detecting falls.<n>We design a service-oriented architecture that leverages Ultra-wideband (UWB) radar sensors as an IoT health-sensing service, ensuring privacy and minimal intrusion.
• Score: 1.4061979259370276
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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