docs/data_format.md

Data Format

This document defines the public format for the ready-to-use preprocessed HAR dataset release.

Repository Layout

datasets/{dataset_slug}/*.npy
archives/*.zip
metadata/datasets.yaml
metadata/manifest.csv

The current partial release uses these folder slugs:

• uci
• unimib
• pamap2
• wisdm
• oppo
• WSBHA

The planned full release can add more folders such as usc_had, flaap, hapt, mhealth, and dsads.

NumPy Array Convention

Each sample array is already windowed and split. Most feature tensors follow:

num_windows x timesteps x channels

The exact shape and dtype of every uploaded file are listed in:

metadata/partial_upload_2026-05-14_manifest.csv

For example:

from huggingface_hub import hf_hub_download
import numpy as np

x_path = hf_hub_download(
    repo_id="shenjianmozhu/preprocessed-har-datasets",
    repo_type="dataset",
    filename="datasets/uci/x_train.npy",
)
X = np.load(x_path)
print(X.shape)

Recommended Standard Names

When adding new processed datasets, prefer one of these patterns:

• x_train.npy, y_train.npy, x_test.npy, y_test.npy
• training_data.npy, training_labels.npy, testing_data.npy, testing_labels.npy

If a dataset has validation files, use explicit names such as x_val.npy and y_val.npy.

Manifest

metadata/*manifest.csv should include one row per released file:

relative_path,dataset_dir,file_name,shape,dtype,size_bytes

The manifest should be regenerated after every data change.