WHAR Datasets: An Open Source Library for Wearable Human Activity Recognition
This work addresses a domain-specific problem for researchers in wearable human activity recognition by providing a tool to improve reproducibility and efficiency, though it is incremental as it builds on existing datasets and models.
The authors tackled the lack of standardization in Wearable Human Activity Recognition (WHAR) datasets by introducing an open-source library that simplifies data handling with a standardized format and configuration-driven design, achieving speedups of up to 3.8x in preprocessing and enabling reproducible workflows with minimal manual intervention.
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.