用虚拟传感器数据提升动作识别，尤其在数据少时效果显著。
Scaling Human Activity Recognition: A Comparative Evaluation of Synthetic Data Generation and Augmentation Techniques
- 通过视频和语言生成虚拟IMU数据，替代真实采集
- 在小数据场景下，虚拟数据比真实或增强数据性能更好
- 为不同需求提供生成策略选择建议，适合数据受限研究
人体动作识别（HAR）常受限于标注数据稀缺，因真实数据收集成本高、难度大。为缓解此问题，近年研究探索通过跨模态迁移生成虚拟惯性测量单元（IMU）数据。视频与语言驱动的生成管道各有优势，但假设条件与计算开销不同，且其相对于传统传感器级数据增强的效果尚不明确。本文首次直接对比两种虚拟IMU生成方法与经典数据增强技术。我们构建了一个大规模虚拟IMU数据集，涵盖来自Kinetics-400的100种多样化动作，并在22个身体部位模拟传感器信号。三种生成策略在四个基准HAR数据集（UTD-MHAD, PAMAP2, HAD-AW）上，使用四种主流模型进行评估。结果表明，虚拟IMU数据在有限数据条件下显著优于仅使用真实或增强数据，大幅提升识别性能。本文提供实际策略选择指导，并揭示各方法的优劣势。
原文摘要 · Abstract (English)
Human activity recognition (HAR) is often limited by the scarcity of labeled datasets due to the high cost and complexity of real-world data collection. To mitigate this, recent work has explored generating virtual inertial measurement unit (IMU) data via cross-modality transfer. While video-based and language-based pipelines have each shown promise, they differ in assumptions and computational cost. Moreover, their effectiveness relative to traditional sensor-level data augmentation remains unclear. In this paper, we present a direct comparison between these two virtual IMU generation approaches against classical data augmentation techniques. We construct a large-scale virtual IMU dataset spanning 100 diverse activities from Kinetics-400 and simulate sensor signals at 22 body locations. The three data generation strategies are evaluated on benchmark HAR datasets (UTD-MHAD, PAMAP2, HAD-AW) using four popular models. Results show that virtual IMU data significantly improves performance over real or augmented data alone, particularly under limited-data conditions. We offer practical guidance on choosing data generation strategies and highlight the distinct advantages and disadvantages of each approach.
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