Abstract
In this paper, we are using comprehensively review the ways in which Large Language Models (LLMs) advance activity recognition systems, discuss the challenges of implementing LLMs, and compare results between LLM-based methods and traditional approaches. We study the basic concepts of LLMs, subsequently, we systematically analyze the researches that have used LLMs for activity recognition, along with the areas related to these tasks, including object detection and speech recognition, since activity recognition can incorporate object detection and speech recognition techniques in its process to improve accuracy and provide a more comprehensive contextual understanding of human activities. We analyze the insights from 26 related research works using the Systematic Literature Review (SLR) approach. By synthesizing recent research, this review shows that LLMs can be applied in various stages of the activity recognition process, where 10% of surveyed paper are implemented at the data collection stage, 10% at the data preprocessing stage, 50% at the feature extraction stage, and 30% papers at the model training stage. Therefore, the data collection and data preprocessing stages allow for more in-depth exploration of opportunities to integrate LLMs at both stages. Moreover, LLMs offer several advantages over traditional methods, including efficient feature extraction, superior performance compared to widely used techniques, robustness across a wide range of data sets, and important enhancements that lead to state-of-the-art performance.
https://ror.org/02278tr80 
