Are You Being Tracked? Discover the Power of Zero-Shot Trajectory
  Tracing with LLMs!
Huanqi Yang Sijie Ji Rucheng Wu Weitao Xu
There is a burgeoning discussion around the capabilities of Large Language
Models (LLMs) in acting as fundamental components that can be seamlessly
incorporated into Artificial Intelligence of Things (AIoT) to interpret complex
trajectories. This study introduces LLMTrack, a model that illustrates how LLMs
can be leveraged for Zero-Shot Trajectory Recognition by employing a novel
single-prompt technique that combines role-play and think step-by-step
methodologies with unprocessed Inertial Measurement Unit (IMU) data. We
evaluate the model using real-world datasets designed to challenge it with
distinct trajectories characterized by indoor and outdoor scenarios. In both
test scenarios, LLMTrack not only meets but exceeds the performance benchmarks
set by traditional machine learning approaches and even contemporary
state-of-the-art deep learning models, all without the requirement of training
on specialized datasets. The results of our research suggest that, with
strategically designed prompts, LLMs can tap into their extensive knowledge
base and are well-equipped to analyze raw sensor data with remarkable
effectiveness.