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.
@article{arxiv.2508.16604,
title = {WHAR Datasets: An Open Source Library for Wearable Human Activity Recognition},
author = {Maximilian Burzer and Tobias King and Till Riedel and Michael Beigl and Tobias Röddiger},
journal= {arXiv preprint arXiv:2508.16604},
year = {2025}
}
Comments
8 pages, 7 figures, to appear in Companion of the 2025 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp), OpenWearables Workshop (accepted paper), updated formatting of authors and headings