Curated list of HAR datasets

A curated list of preprocessed & ready to use under a minute 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:

• x a single array of size [sample count, temporal length, sensor channel count], 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 meta keys.
• y a single array of size [sample count] with integer values for target classes (zero-based). Metadata contains the names of the target classes.
• meta contain various metadata, depends on the dataset (original length before padding, subject no., trial no., etc.)

Usage example

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']

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
  as preprocessed by Jordao et al. originally in WearableSensorData
  (FNOW, LOSO, LOTO and SNOW prefixed .h5 files)