Abstract
Wireless signal-based human sensing technologies, such as WiFi, millimeter-wave (mmWave) radar, and Radio Frequency Identification (RFID), enable the detection and interpretation of human presence, posture, and activities, thereby providing critical support for applications in public security, healthcare, and smart environments. These technologies exhibit notable advantages due to their non-contact operation and environmental adaptability; however, existing systems often fail to leverage the textual information inherent in datasets. To address this, we propose an innovative text-enhanced wireless sensing framework, WiTalk, that seamlessly integrates semantic knowledge through three hierarchical prompt strategies-label-only, brief description, and detailed action description-without requiring architectural modifications or incurring additional data costs. We rigorously validate this framework across three public benchmark datasets: XRF55 for human action recognition (HAR), and WiFiTAL and XRFV2 for WiFi temporal action localization (TAL). Experimental results demonstrate significant performance improvements: on XRF55, accuracy for WiFi, RFID, and mmWave increases by 3.9%, 2.59%, and 0.46%, respectively; on WiFiTAL, the average performance of WiFiTAD improves by 4.98%; and on XRFV2, the mean average precision gains across various methods range from 4.02% to 13.68%. Our codes have been included in this https URL.
Abstract (translated)
基于无线信号的人体感知技术，如WiFi、毫米波（mmWave）雷达和射频识别（RFID），能够检测并解释人体的存在、姿势及活动情况，从而为公共安全、医疗保健和智能环境的应用提供关键支持。这些技术由于其非接触操作能力和环境适应性而具有显著优势；然而，现有的系统常常未能充分利用数据集中的文本信息。为此，我们提出了一种创新的增强型无线感知框架WiTalk，在不进行架构修改或增加额外数据成本的情况下，通过三种层次化的提示策略——仅标签、简短描述和详细动作描述——无缝地整合语义知识。我们在三个公开基准数据集中严格验证了该框架的有效性：XRF55用于人体行为识别（HAR），以及WiFiTAL和XRFV2用于WiFi时间动作定位（TAL）。实验结果显示性能显著提升：在XRF55上，对于WiFi、RFID和mmWave的准确率分别提高了3.9%、2.59% 和0.46%；在WiFiTAL上，WiFiTAD的平均性能提升了4.98%；而在XRFV2上，不同方法的平均精度增益范围从4.02%到13.68%。我们的代码已包含在此链接中：https URL。
URL
https://arxiv.org/abs/2504.14621
