Unsupervised Human Activity Recognition Via Large Language Models and Iterative Evolution

Human activity recognition (HAR) is crucial for health monitoring and disease diagnosis in Internet-of-Things environments. However, existing HAR approaches either suffer from poor accuracy or achieve high accuracy at the expense of costly manual annotations. To overcome the challenge above, we propose a novel method named LLMIE-UHAR that that leverages LLMs and Iterative Evolution to realize Unsupervised HAR. Specifically, with our designed prompt engineering mechanism, we employ large language models to fuse both contextual and semantic information, and annotate key samples selected by a clustering algorithm. Moreover, LLMIE-UHAR enhances the recognition accuracy with iterative evolution of clustering algorithm, large language models and the neural network based recognition model. Experiments conducted on the public ARAS datasets show the efficiency of our method, achieving an accuracy of 96.00%. This highlights the practical value of our approach.