TSGen: A Framework for Indoor Trajectory-Semantic Data Synthesis

Abstract

Smart homes are evolving from passive response to proactive service, where understanding the semantic information in user trajectories is essential. However, existing tracking systems output only sparse location sequences, insufficient for higher-level semantic reasoning. The core bottleneck lies in the lack of large-scale annotated datasets, while traditional data collection is costly and raises privacy concerns. We present TSGen, which formalizes indoor trajectory-semantic data synthesis as a structured decomposition problem. We first demonstrate that naive end-to-end LLM approaches are fundamentally limited for this task, then propose a hybrid workflow combining LLM-driven behavior simulation, physics-constrained trajectory synthesis, and a self-refining prompt architecture that evolves from runtime errors, to construct the T-S dataset (14,000 hours of trajectory-semantic pairs). We also implement a hierarchical processing pipeline as an initial baseline for semantic understanding, establishing reference benchmarks for future research. Evaluations cover data quality (user studies, diversity analysis, ablation studies) and baseline performance. On QA tasks over synthetic data and real-world deployments, our baseline significantly outperforms direct LLM methods while reducing computational costs. The open-sourced dataset and codebase provide foundational resources for this emerging research area1.