README.md

metadata

task_categories:
  - other
tags:
  - human-activity-recognition
  - sensor-data
  - time-series
  - out-of-distribution

HAROOD: A Benchmark for Out-of-distribution Generalization in Sensor-based Human Activity Recognition

Paper | GitHub Repository

HAROOD is a modular and reproducible benchmark framework for studying generalization in sensor-based human activity recognition (HAR). It unifies preprocessing pipelines, standardizes four realistic OOD scenarios (cross-person, cross-position, cross-dataset, and cross-time), and implements 16 representative algorithms across CNN and Transformer architectures.

Key Features

• 6 public HAR datasets unified under a single framework.
• 5 realistic OOD scenarios: cross-person, cross-position, cross-dataset, cross-time, and cross-device.
• 16 generalization algorithms spanning Data Manipulation, Representation Learning, and Learning Strategies.
• Backbone support: Includes both CNN and Transformer-based architectures.
• Standardized splits: Provides train/val/test model selection protocols.

Usage

The benchmark is designed to be modular. Below are examples of how to run experiments using the official implementation:

Run with a YAML config

from core import train
results = train(config='./config/experiment.yaml')

Run with a Python dict

from core import train
config_dict = {
    'algorithm': 'CORAL',
    'batch_size': 32,
}
results = train(config=config_dict)

Override parameters

from core import train
results = train(
    config='./config/experiment.yaml',
    lr=2e-3,
    max_epoch=200,
)

Supported Algorithms

The benchmark implements 16 algorithms across three main categories:

• Data Manipulation: Mixup, DDLearn.
• Representation Learning: ERM, DANN, CORAL, MMD, VREx, LAG.
• Learning Strategy: MLDG, RSC, GroupDRO, ANDMask, Fish, Fishr, URM, ERM++.

Citation

If you use HAROOD in your research, please cite the following paper:

@inproceedings{lu2026harood,
  title={HAROOD: A Benchmark for Out-of-distribution Generalization in Sensor-based Human Activity Recognition},
  author={Lu, Wang and Zhu, Yao and Wang, Jindong},
  booktitle={The 32rd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)},
  year={2026}
}