Recent years have witnessed an increasing demand for human fall detection systems. Among all existing methods, Wi-Fi-based fall detection has become one of the most promising solutions due to its pervasiveness. However, when applied to a new domain, existing Wi-Fi-based solutions suffer from severe performance degradation caused by low generalizability. In this paper, we propose XFall, a domain-adaptive fall detection system based on Wi-Fi. XFall overcomes the generalization problem from three aspects. To advance cross-environment sensing, XFall exploits an environment-independent feature called speed distribution profile, which is irrelevant to indoor layout and device deployment. To ensure sensitivity across all fall types, an attention-based encoder is designed to extract the general fall representation by associating both the spatial and temporal dimensions of the input. To train a large model with limited amounts of Wi-Fi data, we design a cross-modal learning framework, adopting a pre-trained visual model for supervision during the training process. We implement and evaluate XFall on one of the latest commercial wireless products through a year-long deployment in real-world settings. The result shows XFall achieves an overall accuracy of 96.8%, with a miss alarm rate of 3.1% and a false alarm rate of 3.3%, outperforming the state-of-the-art solutions in both in-domain and cross-domain evaluation.

中文翻译：

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XFall：具有跨模式监控的基于 Wi-Fi 的域自适应跌倒检测


近年来，对人体跌倒检测系统的需求不断增加。在所有现有方法中，基于 Wi-Fi 的跌倒检测由于其普遍性而成为最有前途的解决方案之一。然而，当应用于新领域时，现有的基于 Wi-Fi 的解决方案会因通用性低而导致性能严重下降。在本文中，我们提出了 XFall，一种基于 Wi-Fi 的域自适应跌倒检测系统。 XFall从三个方面克服了泛化问题。为了推进跨环境传感，XFall 利用了一种独立于环境的功能，称为速度分布曲线，该功能与室内布局和设备部署无关。为了确保所有跌倒类型的敏感性，基于注意力的编码器被设计为通过关联输入的空间和时间维度来提取一般跌倒表示。为了用有限的 Wi-Fi 数据训练大型模型，我们设计了一个跨模态学习框架，在训练过程中采用预训练的视觉模型进行监督。我们通过在现实环境中进行长达一年的部署，在最新的商用无线产品上实施和评估 XFall。结果显示，XFall 的总体准确率达到 96.8%，漏报率为 3.1%，误报率为 3.3%，在域内和跨域评估方面均优于最先进的解决方案。