Abstract
The increasing global aging population has intensified the demand for reliable health monitoring systems, particularly those capable of detecting critical events such as falls among elderly individuals. Traditional fall detection approaches relying on single-modality acceleration data suffer from high false alarm rates, while conventional machine learning methods require extensive hand-crafted feature engineering. This paper proposes a novel multi-modal deep learning framework, MultiModalFallDetector, designed for real-time elderly fall detection using wearable sensors. Our approach integrates multiple innovations: a multi-scale CNN-based feature extractor capturing motion dynamics at varying temporal resolutions; fusion of tri-axial accelerometer, gyroscope, and four-channel physiological signals; incorporation of a multi-head self-attention mechanism for dynamic temporal weighting; adoption of Focal Loss to mitigate severe class imbalance; introduction of an auxiliary activity classification task for regularization; and implementation of transfer learning from UCI HAR to SisFall dataset. Extensive experiments on the SisFall dataset, which includes real-world simulated fall trials from elderly participants (aged 60-85), demonstrate that our framework achieves an F1-score of 98. 7, Recall of 98. 9, and AUC-ROC of 99. 4, significantly outperforming baseline methods including traditional machine learning and standard deep learning approaches. The model maintains sub- 50ms inference latency on edge devices, confirming its suitability for real-time deployment in geriatric care settings.
Abstract (translated)
全球老龄化人口的增长加剧了对可靠健康监测系统的需求，尤其是能够检测老年人跌倒等关键事件的系统。传统依赖单一模态加速度数据的跌倒检测方法误报率较高，而常规机器学习方法则需要大量手工特征工程。本研究提出一种新颖的多模态深度学习框架MultiModalFallDetector，专为使用可穿戴传感器进行实时老年人跌倒检测而设计。该方法整合了多项创新：基于多尺度CNN的特征提取器，可捕获不同时间分辨率的运动动态；融合三轴加速度计、陀螺仪和四通道生理信号；引入多头自注意力机制实现动态时间加权；采用Focal Loss缓解严重的类别不平衡问题；引入辅助活动分类任务进行正则化；并实施从UCI HAR数据集到SisFall数据集的迁移学习。在包含60-85岁老年参与者真实模拟跌倒试验的SisFall数据集上进行的大量实验表明，本框架的F1分数达98.7，召回率达98.9，AUC-ROC值达99.4，显著优于包括传统机器学习和标准深度学习方法在内的基线方案。该模型在边缘设备上保持低于50毫秒的推理延迟，证实其适用于老年护理场景的实时部署。
URL
https://arxiv.org/abs/2603.22313
