Recent progress in Large Language Models (LLMs) has enabled advanced reasoning and zero-shot recognition for human activity understanding with ambient sensor data. However, widely used multi-resident datasets such as CASAS, ARAS, and MARBLE lack natural language context and fine-grained annotation, limiting the full exploitation of LLM capabilities in realistic smart environments. To address this gap, we present MuRAL (Multi-Resident Ambient sensor dataset with natural Language), comprising over 21 hours of multi-user sensor data from 21 sessions in a smart home. MuRAL uniquely features detailed natural language descriptions, explicit resident identities, and rich activity labels, all situated in complex, dynamic, multi-resident scenarios. We benchmark state-of-the-art LLMs on MuRAL for three core tasks: subject assignment, action description, and activity classification. Results show that current LLMs still face major challenges on MuRAL, especially in maintaining accurate resident assignment over long sequences, generating precise action descriptions, and effectively integrating context for activity prediction. The dataset is publicly available at: https://mural.imag.fr/.

翻译：近年来，大型语言模型（LLMs）的进展使得利用环境传感器数据进行人类活动理解的高级推理与零样本识别成为可能。然而，广泛使用的多居民数据集（如CASAS、ARAS和MARBLE）缺乏自然语言上下文与细粒度标注，限制了LLMs在现实智能环境中的充分应用。为弥补这一不足，我们提出了MuRAL（附带自然语言的多居民环境传感器数据集），该数据集包含来自智能家居中21个会话、总计超过21小时的多用户传感器数据。MuRAL的独特之处在于其提供了详细的自然语言描述、明确的居民身份标识以及丰富的活动标签，且所有数据均置于复杂、动态的多居民场景中。我们在MuRAL上对当前最先进的LLMs进行了三项核心任务的基准测试：主体分配、动作描述和活动分类。结果表明，现有LLMs在MuRAL上仍面临重大挑战，尤其是在长序列中保持准确的居民分配、生成精确的动作描述以及有效整合上下文进行活动预测方面。该数据集已公开提供：https://mural.imag.fr/。