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.
View on arXiv@article{chen2025_2504.20505,
title={ MuRAL: A Multi-Resident Ambient Sensor Dataset Annotated with Natural Language for Activities of Daily Living },
author={ Xi Chen and Julien Cumin and Fano Ramparany and Dominique Vaufreydaz },
journal={arXiv preprint arXiv:2504.20505},
year={ 2025 }
}