WHAR Datasets: An Open Source Library for Wearable Human Activity Recognition
/ Authors
/ Abstract
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 3.8× using multiprocessing. We hope this library contributes to more efficient, reproducible, and comparable WHAR research.
Journal: Companion of the 2025 ACM International Joint Conference on Pervasive and Ubiquitous Computing