Towards LLM-Powered Ambient Sensor Based Multi-Person Human Activity Recognition
This addresses activity recognition for healthcare and security applications, but it is incremental as it adapts existing LLM techniques to a known bottleneck.
The paper tackles multi-person human activity recognition using ambient sensors by proposing LAHAR, a framework based on large language models with prompt engineering, achieving comparable accuracy to state-of-the-art methods on the ARAS dataset.
Human Activity Recognition (HAR) is one of the central problems in fields such as healthcare, elderly care, and security at home. However, traditional HAR approaches face challenges including data scarcity, difficulties in model generalization, and the complexity of recognizing activities in multi-person scenarios. This paper proposes a system framework called LAHAR, based on large language models. Utilizing prompt engineering techniques, LAHAR addresses HAR in multi-person scenarios by enabling subject separation and action-level descriptions of events occurring in the environment. We validated our approach on the ARAS dataset, and the results demonstrate that LAHAR achieves comparable accuracy to the state-of-the-art method at higher resolutions and maintains robustness in multi-person scenarios.