开源工具库统一可穿戴动作识别数据集，提升研究复现效率。
WHAR Datasets: An Open Source Library for Wearable Human Activity Recognition
- 标准化数据格式+配置驱动设计，减少手动操作
- 支持9个主流数据集，多进程加速达3.8倍
- 兼容主流框架，适合做基准测试和复现
可穿戴人类动作识别（WHAR）数据集缺乏标准化，限制了研究的可复现性、可比性和效率。我们推出WHAR datasets，一个开源库，通过标准化数据格式和配置驱动设计，实现低干预、高效率、可复现的处理流程。该库目前支持9个广泛使用的数据集，兼容PyTorch与TensorFlow，且易于扩展至新数据集。为验证其有效性，我们在包含数据集上训练了TinyHar和MLP-HAR两个前沿模型，近似复现了已有结果。此外，预处理性能评估显示，使用多进程可获得最高3.8倍的速度提升。我们期望该库能推动更高效、可复现、可比的WHAR研究。
原文摘要 · Abstract (English)
The lack of standardization across Wearable Human Activity Recognition (WHAR) datasets limits reproducibility, comparability, and research efficiency. We introduce WHAR datasets, an open-source library designed to simplify WHAR data handling through a standardized data format and a configuration-driven design, enabling reproducible and computationally efficient workflows with minimal manual intervention. The library currently supports 9 widely-used datasets, integrates with PyTorch and TensorFlow, and is easily extensible to new datasets. To demonstrate its utility, we trained two state-of-the-art models, TinyHar and MLP-HAR, on the included datasets, approximately reproducing published results and validating the library's effectiveness for experimentation and benchmarking. Additionally, we evaluated preprocessing performance and observed speedups of up to 3.8x using multiprocessing. We hope this library contributes to more efficient, reproducible, and comparable WHAR research.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。