HAROOD: A Benchmark for Out-of-distribution Generalization in Sensor-based Human Activity Recognition
Paper
•
2512.10807
•
Published
Dataset Viewer
The dataset viewer is not available for this subset.
Cannot get the split names for the config 'default' of the dataset.
Exception: SplitsNotFoundError
Message: The split names could not be parsed from the dataset config.
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 289, in get_dataset_config_info
for split_generator in builder._split_generators(
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 91, in _split_generators
inferred_arrow_schema = pa.concat_tables(pa_tables, promote_options="default").schema
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "pyarrow/table.pxi", line 6319, in pyarrow.lib.concat_tables
File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
pyarrow.lib.ArrowTypeError: Unable to merge: Field npy has incompatible types: list<item: list<item: list<item: double>>> vs list<item: list<item: double>>: Unable to merge: Field item has incompatible types: list<item: list<item: double>> vs list<item: double>: Unable to merge: Field item has incompatible types: list<item: double> vs double
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 65, in compute_split_names_from_streaming_response
for split in get_dataset_split_names(
^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 343, in get_dataset_split_names
info = get_dataset_config_info(
^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 294, in get_dataset_config_info
raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
HAROOD: A Benchmark for Out-of-distribution Generalization in Sensor-based Human Activity Recognition
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}
}
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