There is growing interest in enabling wireless sensing systems to interpret human motion from unsegmented wireless signals; however, existing CSI-based applications rely heavily on accurate signal segmentation and predefined action labels, limiting their applicability in zero-shot scenarios. We present WirelessSenseLLM, a language-driven framework that leverages large language models (LLMs) to enable zero-shot human motion understanding from unsegmented Wi-Fi Channel State Information (CSI). To bridge the modality gap between time-series CSI and discrete language representations, we introduce a CSI-to-Language Adapter and a cross-modal projection mechanism that maps CSI features into a language-aligned semantic space. This design enables the generation of fine-grained natural language descriptions of sequential and overlapping human motions, supporting downstream reasoning without segmented training data. We address two core technical challenges: modality mismatch between CSI features and language embeddings, and overlapping actions in unsegmented CSI streams. Extensive experiments demonstrate strong performance in zero-shot action understanding (92% accuracy and 91% F1-score), language-based reasoning quality (30% factual and 15% reasoning improvements), and multi-person motion explanation with an average 12.33% improvement over prior methods. These results highlight WirelessSenseLLM's effectiveness for robust and interpretable human motion understanding from CSI signals.

翻译：无线感知系统从非分割无线信号中解释人体运动的兴趣日益增长，然而现有基于CSI的应用严重依赖准确的信号分割和预定义动作标签，限制了其在零样本场景中的适用性。我们提出WirelessSenseLLM，一个语言驱动框架，利用大语言模型（LLMs）实现从非分割Wi-Fi信道状态信息（CSI）中进行零样本人体运动理解。为弥合时序CSI与离散语言表示之间的模态差距，我们引入CSI到语言适配器（CSI-to-Language Adapter）及跨模态投影机制，将CSI特征映射至语言对齐的语义空间。这种设计能够生成连续和重叠人体运动的细粒度自然语言描述，支持无需分割训练数据的下游推理。我们解决两个核心技术挑战：CSI特征与语言嵌入之间的模态不匹配，以及非分割CSI流中的重叠动作。大量实验证明，该模型在零样本动作理解（准确率92%，F1分数91%）、基于语言的推理质量（事实性改进30%，推理改进15%）以及多人运动解释方面表现出色，平均较先前方法提升12.33%。这些结果凸显了WirelessSenseLLM在从CSI信号中进行鲁棒且可解释的人体运动理解方面的有效性。