  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.
