Privacy-aware IoT Fall Detection Services For Aging in Place

TL;DR

This paper introduces a privacy-preserving IoT fall detection framework using UWB radar sensors and a generative transformer model, achieving high accuracy in detecting falls among the elderly.

Contribution

It presents a novel FDaaS architecture with privacy-focused sensors and a data augmentation method using FD-GPT for improved fall detection.

Findings

Achieved 90.72% accuracy in fall detection.

Developed a comprehensive dataset mimicking elderly routines.

Demonstrated effectiveness of the proposed model with high precision.

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.