shenjianmozhu/preprocessed-har-datasets

Ready-to-Use Preprocessed HAR Datasets

This dataset repository provides ready-to-use, preprocessed Human Activity Recognition (HAR) datasets. The released files are already windowed, split, and stored as NumPy arrays, so users can download them and run model comparisons directly.

The goal is to make HAR model comparison easier by releasing a consistent set of preprocessed windows, split files, labels, and metadata. The collection covers smartphone-based sensing, wearable IMU sensing, daily activities, fall-related motions, and multimodal wearable settings under different sensor layouts and label granularities.

Current Dataset Folders

The browsable download layout is:

datasets/
  uci/
  unimib/
  pamap2/
  wisdm/
  oppo/
  WSBHA/
archives/
  processed_har_npy_partial_2026-05-14.zip
metadata/
  partial_upload_2026-05-14_manifest.csv

The datasets/ folders are intended for direct per-dataset downloads. The archives/ zip is a convenient one-file mirror of the current uploaded batch.

Quick Download

from huggingface_hub import hf_hub_download, snapshot_download
import numpy as np

x_train_path = hf_hub_download(
    repo_id="shenjianmozhu/preprocessed-har-datasets",
    repo_type="dataset",
    filename="datasets/uci/x_train.npy",
)
y_train_path = hf_hub_download(
    repo_id="shenjianmozhu/preprocessed-har-datasets",
    repo_type="dataset",
    filename="datasets/uci/y_train.npy",
)

X_train = np.load(x_train_path)
y_train = np.load(y_train_path)
print(X_train.shape, y_train.shape)

To download everything:

local_dir = snapshot_download(
    repo_id="shenjianmozhu/preprocessed-har-datasets",
    repo_type="dataset",
    local_dir="preprocessed-har-datasets",
)

Dataset Notes

Folder	Main files	Notes
datasets/uci	x_train.npy, y_train.npy, x_test.npy, y_test.npy	UCI-HAR-style train/test arrays.
datasets/unimib	training_data.npy, training_labels.npy, testing_data.npy, testing_labels.npy	UniMiB-style preprocessed arrays.
datasets/pamap2	train_X_new.npy, train_y_new.npy, total_pamap2_valtestx.npy, total_pamap2_valtesty.npy	Train plus validation/test batch.
datasets/wisdm	x_train.npy, y_train.npy, x_test.npy, y_test.npy	WISDM-style preprocessed arrays.
datasets/oppo	data_train_one.npy, label_train_onehot.npy, data_test_one.npy, label_test_onehot.npy	OPPORTUNITY-style arrays with one-hot labels.
datasets/WSBHA	training_data.npy, training_labels.npy, testing_data.npy, testing_labels.npy	Current uploaded WSBHA folder, preserved as provided.

File-level shapes and dtypes are listed in metadata/partial_upload_2026-05-14_manifest.csv.

Google Drive Mirror

A Google Drive mirror can be used as a backup download route for users who prefer browser-based downloads. When public sharing is enabled, the link should be listed here and in the GitHub README as an optional mirror, while Hugging Face remains the canonical dataset host.

Citation

Please cite this data release, the original datasets, and any relevant HAR method papers.

@misc{teng_preprocessed_har_datasets_2026,
  title        = {Ready-to-Use Preprocessed HAR Datasets},
  author       = {Teng, Qi and collaborators},
  year         = {2026},
  howpublished = {\url{https://huggingface.co/datasets/shenjianmozhu/preprocessed-har-datasets}},
  note         = {Preprocessed fixed-window NumPy arrays for HAR benchmarking}
}

Licensing

This repository contains processed versions of public datasets. The original datasets retain their own licenses, citation requirements, and redistribution terms. The license: other metadata is intentional because the collection is license-mixed. Users must comply with the terms of each original dataset.

If any source dataset does not permit redistribution of derived/preprocessed files, the corresponding processed files should be removed from this Hugging Face repository and replaced by preprocessing scripts plus links to the original source.

Responsible Use

The data are intended for research on HAR, time-series classification, wearable sensing, and benchmark reproducibility. Users should avoid using the data for identity inference, health-status inference, or surveillance applications beyond the scope of the original datasets and consent protocols.