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.

翻译：关于大型语言模型（LLMs）作为可无缝集成到人工智能物联网（AIoT）中以解读复杂轨迹的基础组件的能力，正引发日益热烈的讨论。本研究提出了LLMTrack模型，通过一种结合角色扮演与逐步思考方法论的新型单提示技术，利用未经处理的惯性测量单元（IMU）数据，展示了如何运用LLMs实现零样本轨迹识别。我们使用真实世界数据集对模型进行评估，这些数据集通过室内外场景中截然不同的轨迹设计来考验模型。在两种测试场景中，LLMTrack不仅达到甚至超越了传统机器学习方法及当代最先进深度学习模型所设定的性能基准，且无需在专门数据集上进行训练。我们的研究结果表明，通过策略性设计的提示，LLMs能够调动其广博的知识库，以显著有效性分析原始传感器数据。