Recent advances in Large Language Models (LLMs) have shown promising potential for human activity recognition (HAR) using ambient sensors, especially through natural language reasoning and zero-shot learning. However, existing datasets such as CASAS, ARAS, and MARBLE were not originally designed with LLMs in mind and therefore lack the contextual richness, complexity, and annotation granularity required to fully exploit LLM capabilities. In this paper, we introduce MuRAL, the first Multi-Resident Ambient sensor dataset with natural Language, comprising over 21 hours of multi-user sensor data collected from 21 sessions in a smart-home environment. MuRAL is annotated with fine-grained natural language descriptions, resident identities, and high-level activity labels, all situated in dynamic, realistic multi-resident settings. We benchmark MuRAL using state-of-the-art LLMs for three core tasks: subject assignment, action description, and activity classification. Our results demonstrate that while LLMs can provide rich semantic interpretations of ambient data, current models still face challenges in handling multi-user ambiguity and under-specified sensor contexts. We release MuRAL to support future research on LLM-powered, explainable, and socially aware activity understanding in smart environments. For access to the dataset, please reach out to us via the provided contact information. A direct link for dataset retrieval will be made available at this location in due course.

本研究解决了现有环境传感器数据集中缺乏自然语言标注和上下文丰富性的问题。我们提出了MuRAL数据集，它包含来自智能家居环境中21个会话的多用户传感器数据，提供了细粒度的自然语言描述和活动标签。研究表明，尽管大型语言模型在解析环境数据方面展现出潜力，但仍存在多用户模糊性和传感器上下文定义不足的挑战。

MuRAL：一个使用自然语言标注的多住居民环境传感器数据集，用于日常活动识别