WHAR Datasets: An Open Source Library for Wearable Human Activity Recognition

TL;DR

This paper introduces WHAR datasets, an open-source library that standardizes data handling for wearable human activity recognition, improving reproducibility, efficiency, and benchmarking capabilities across multiple datasets.

Contribution

The paper presents a standardized, extensible library supporting multiple datasets, integrated with major ML frameworks, and validated through training state-of-the-art models.

Findings

Achieved up to 3.8x speedup in preprocessing with multiprocessing.

Successfully trained models that approximately reproduce published results.

Supported 9 widely-used datasets with easy extensibility.

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 3.8x using multiprocessing. We hope this library contributes to more efficient, reproducible, and comparable WHAR research.