DOI: 10.5281/zenodo.3831957
title: Curated list of HAR datasets

description: A curated list of <em>preprocessed</em> & <em>ready to use under a minute</em> Human Activity Recognition datasets. All the datasets are preprocessed in HDF5 format, created using the h5py python library. Scripts used for data preprocessing are provided as well (Load.ipynb and load_jordao.py) Each HDF5 file contains at least the keys: <code>x</code> a single array of size <code>[sample count, temporal length, sensor channel count]</code>, contains the actual sensor data. Metadata contains the names of individual sensor channel count. All samples are zero-padded for constant length in the file, original lengths before padding available under the <code>meta</code> keys. <code>y</code> a single array of size <code>[sample count]</code> with integer values for target classes (zero-based). Metadata contains the names of the target classes. <code>meta</code> contain various metadata, depends on the dataset (original length before padding, subject no., trial no., etc.) Usage example <pre><code>import h5py with h5py.File(f'data/waveglove_multi.h5', 'r') as h5f: x = h5f['x'] y = h5f['y']['class'] print(f'WaveGlove-multi: {x.shape[0]} samples') print(f'Sensor channels: {h5f["x"].attrs["channels"]}') print(f'Target classes: {h5f["y"].attrs["labels"]}') first_sample = x[0] # Output: # WaveGlove-multi: 10044 samples # Sensor channels: ['acc1-x' 'acc1-y' 'acc1-z' 'gyro1-x' 'gyro1-y' 'gyro1-z' 'acc2-x' # 'acc2-y' 'acc2-z' 'gyro2-x' 'gyro2-y' 'gyro2-z' 'acc3-x' 'acc3-y' # 'acc3-z' 'gyro3-x' 'gyro3-y' 'gyro3-z' 'acc4-x' 'acc4-y' 'acc4-z' # 'gyro4-x' 'gyro4-y' 'gyro4-z' 'acc5-x' 'acc5-y' 'acc5-z' 'gyro5-x' # 'gyro5-y' 'gyro5-z'] # Target classes: ['null' 'hand swipe left' 'hand swipe right' 'pinch in' 'pinch out' # 'thumb double tap' 'grab' 'ungrab' 'page flip' 'peace' 'metal'] </code></pre> Current list of datasets: WaveGlove-single (waveglove_single.h5) WaveGlove-multi (waveglove_multi.h5) uWave (uwave.h5) OPPORTUNITY (opportunity.h5) PAMAP2 (pamap2.h5) SKODA (skoda.h5) MHEALTH (non overlapping windows) (mhealth.h5) Six datasets with all four predefined train/test folds<br> as preprocessed by Jordao et al. originally in WearableSensorData<br> (FNOW, LOSO, LOTO and SNOW prefixed .h5 files)
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