The proliferation of wearable technology enables the generation of vast amounts of sensor data, offering significant opportunities for advancements in health monitoring, activity recognition, and personalized medicine. However, the complexity and volume of this data present substantial challenges in data modeling and analysis, which have been tamed with approaches spanning time series modeling to deep learning techniques. The latest frontier in this domain is the adoption of Large Language Models (LLMs), such as GPT-4 and Llama, for data analysis, modeling, understanding, and generation of human behavior through the lens of wearable sensor data. This survey explores current trends and challenges in applying LLMs for sensor-based human activity recognition and behavior modeling. We discuss the nature of wearable sensors data, the capabilities and limitations of LLMs to model them and their integration with traditional machine learning techniques. We also identify key challenges, including data quality, computational requirements, interpretability, and privacy concerns. By examining case studies and successful applications, we highlight the potential of LLMs in enhancing the analysis and interpretation of wearable sensors data. Finally, we propose future directions for research, emphasizing the need for improved preprocessing techniques, more efficient and scalable models, and interdisciplinary collaboration. This survey aims to provide a comprehensive overview of the intersection between wearable sensors data and LLMs, offering insights into the current state and future prospects of this emerging field.

翻译：可穿戴技术的普及催生了海量传感器数据的生成，为健康监测、活动识别和个性化医疗领域的进步提供了重要机遇。然而，此类数据的复杂性与规模给数据建模与分析带来了巨大挑战，现有方法涵盖从时间序列建模到深度学习技术。该领域的最新前沿是采用大型语言模型（如GPT-4和Llama），通过可穿戴传感器数据的视角进行数据分析、建模、理解及人类行为生成。本综述探讨了应用LLM进行基于传感器的人体活动识别与行为建模的当前趋势与挑战。我们讨论了可穿戴传感器数据的特性、LLM对其建模的能力与局限，以及LLM与传统机器学习技术的融合。同时，我们指出了数据质量、计算需求、可解释性与隐私问题等关键挑战。通过分析案例研究与成功应用，我们强调了LLM在增强可穿戴传感器数据分析与解释方面的潜力。最后，我们提出了未来研究方向，强调需要改进预处理技术、开发更高效可扩展的模型，并加强跨学科合作。本综述旨在全面概述可穿戴传感器数据与LLM的交叉领域，为此新兴领域的现状与未来前景提供深入见解。