用雷达传感与生成模型实现隐私保护的老人跌倒检测服务
Privacy-aware IoT Fall Detection Services For Aging in Place
- 基于超宽带雷达构建隐私友好型跌倒检测服务架构
- 在真实数据集上达到90.72%准确率和89.33%精确率
- 适合关注居家养老安全与数据隐私的研究者
跌倒检测对支持不断增长的老龄人口至关重要，预计到2050年全球老年人口将达21亿。现有方法常面临数据稀缺或隐私泄露问题。本文提出一种基于物联网的跌倒检测即服务（FDaaS）框架，通过超宽带（UWB）雷达传感器作为健康感知服务，实现隐私保护与低侵入性监测。针对数据稀缺问题，采用跌倒检测生成式预训练变换器（FD-GPT）结合增强技术。设计并实施协议采集了涵盖老年人日常活动与跌倒事件的综合性数据集，真实还原其生活常态。在该数据集上严格评估并比较多种模型，实验结果表明，本方法在区分跌倒事件与日常生活活动方面达到90.72%的准确率和89.33%的精确率。
原文摘要 · Abstract (English)
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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