可穿戴技术的普及使得能够产生大量的传感器数据,为健康监测的进步提供了重要的机会,活动识别,个性化医疗。然而,这些数据的复杂性和数量在数据建模和分析中提出了巨大的挑战,这些问题已经通过跨越时间序列建模到深度学习技术的方法得到了解决。该领域的最新前沿是采用大型语言模型(LLM),比如GPT-4和Llama,为了进行数据分析,建模,理解,并通过可穿戴传感器的镜头监测人体行为数据。本调查探讨了将LLM应用于基于传感器的人类活动识别和行为建模的当前趋势和挑战。我们讨论了可穿戴传感器数据的性质,LLM在建模时的能力和局限性,以及它们与传统机器学习技术的集成。我们还确定了关键挑战,包括数据质量,计算要求,可解释性,和隐私问题。通过研究案例和成功的应用,我们强调了LLM在增强可穿戴传感器数据的分析和解释方面的潜力。最后,我们提出了未来的研究方向,强调需要改进预处理技术,更高效和可扩展的模型,跨学科合作。这项调查旨在全面概述可穿戴传感器数据与LLM之间的交集,提供对这一新兴领域的现状和未来前景的见解。
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 these data present substantial challenges in data modeling and analysis, which have been addressed 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 human behavior monitoring through the lens of wearable sensor data. This survey explores the current trends and challenges in applying LLMs for sensor-based human activity recognition and behavior modeling. We discuss the nature of wearable sensor data, the capabilities and limitations of LLMs in modeling 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 sensor 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 sensor data and LLMs, offering insights into the current state and future prospects of this emerging field.