This library provides data handling support for popular WHAR (Wearable Human Activity Recognition) datasets including
- downloading from original source
- parsing into a standardized data format
- configuration-driven preprocessing, splitting, normalization, and more
- integration with pytorch and tensorflow
pip install "git+https://github.com/teco-kit/whar-datasets.git"
This installs the library into the active environment.
from whar_datasets.adapters.torch_adapter import TorchAdapter
from whar_datasets.support.getter import WHARDatasetID, get_dataset_cfg
# initialize dataset config given a dataset id
cfg = get_dataset_cfg(dataset_id=WHARDatasetID.WISDM)
# initialize dataset and preprocess
dataset = TorchAdapter(cfg=cfg)
dataset.preprocess()
# postprocess for a given split and get dataloaders
dataset.postprocess(split_group_index=0)
dataloaders = dataset.get_dataloaders(batch_size=32)Not yet natively supported WHAR datasets can be integrated via a custom configuration (with parser).
| Supported | Name | Year | Paper | Citations |
|---|---|---|---|---|
| ✅ | WISDM | 2010 | Activity Recognition using Cell Phone Accelerometers | 3862 |
| ✅ | UCI-HAR | 2013 | A Public Domain Dataset for Human Activity Recognition using Smartphones | 3372 |
| ✅ | PAMAP2 | 2012 | Introducing a New Benchmarked Dataset for Activity Monitoring | 1758 |
| ✅ | OPPORTUNITY | 2010 | Collecting complex activity datasets in highly rich networked sensor environments | 1024 |
| ⬜ | HHAR | 2015 | Smart Devices are Different: Assessing and Mitigating Mobile Sensing Heterogeneities for Activity Recognition | 1019 |
| ⬜ | UTD-MHAD | 2015 | UTD-MHAD: A Multimodal Dataset for Human Action Recognition Utilizing a Depth Camera and a Wearable Inertial Sensor | 997 |
| ✅ | MHEALTH | 2014 | mHealthDroid: A Novel Framework for Agile Development of Mobile Health Applications | 887 |
| ✅ | DSADS | 2010 | Comparative study on classifying human activities with miniature inertial and magnetic sensors | 780 |
| ⬜ | USC-HAD | 2012 | USC-HAD: A Daily Activity Dataset for Ubiquitous Activity Recognition Using Wearable Sensors | 753 |
| ⬜ | SAD | 2014 | Fusion of Smartphone Motion Sensors for Physical Activity Recognition | 752 |
| ⬜ | UniMiB-SHAR | 2017 | Unimib shar: a dataset for human activity recognition using acceleration data from smartphones | 712 |
| ✅ | Daphnet | 2009 | Ambulatory monitoring of freezing of gait in Parkinson’s disease | 652 |
| ⬜ | RealWorld | 2016 | On-body Localization of Wearable Devices: An Investigation of Position-Aware Activity Recognition | 482 |
| ⬜ | ExtraSensory | 2016 | Recognizing Detailed Human Context In-the-Wild from Smartphones and Smartwatches | 402 |
| ⬜ | MobiAct | 2016 | The MobiAct dataset: recognition of activities of daily living using smartphones | 364 |
| ✅ | MotionSense | 2019 | Mobile Sensor Data Anonymization | 345 |
| ⬜ | PARDUSS | 2013 | Towards physical activity recognition using smartphone sensors | 345 |
| ⬜ | SWELL-KW | 2014 | The SWELL Knowledge Work Dataset for Stress and User Modeling Research | 339 |
| ⬜ | SHL | 2018 | The University of Sussex-Huawei Locomotion and Transportation Dataset for Multimodal Analytics with Mobile Devices | 317 |
| ⬜ | DA | 2012 | Recognizing Human Activities User-independently on Smartphones Based on Accelerometer Data | 302 |
| ⬜ | UMAFall | 2017 | Umafall: A multisensor dataset for the research on automatic fall detection | 243 |
| ⬜ | REALDISP | 2014 | Dealing with the Effects of Sensor Displacement in Wearable Activity Recognition | 216 |
| ⬜ | RealLifeHAR | 2020 | A Public Domain Dataset for Real-Life Human Activity Recognition Using Smartphone Sensors | 208 |
| ⬜ | WISDM-19 | 2019 | WISDM: Smartphone and Smartwatch Activity and Biometrics Dataset | 198 |
| ✅ | KU-HAR | 2021 | KU-HAR: An open dataset for heterogeneous human activity recognition | 187 |
| ⬜ | HASC-Challenge | 2011 | Hasc challenge: gathering large scale human activity corpus for the real-world activity understandings | 157 |
| ⬜ | HuGaDB | 2018 | HuGaDB: Human Gait Database for Activity Recognition from Wearable Inertial Sensor Networks | 154 |
| ⬜ | HARTH | 2021 | HARTH: A Human Activity Recognition Dataset for Machine Learning | 132 |
| ⬜ | MobiFall | 2014 | The MobiFall Dataset: Fall Detection and Classification with a Smartphone | 128 |
| ⬜ | FallAllD | 2020 | FallAllD: An Open Dataset of Human Falls and Activities of Daily Living for Classical and Deep Learning Applications | 115 |
| ⬜ | w-HAR | 2020 | w-HAR: An Activity Recognition Dataset and Framework Using Low-Power Wearable Devices | 98 |
| ⬜ | HAR70+ | 2021 | A machine learning classifier for detection of physical activity types and postures during free-living | 55 |
| ⬜ | TNDA-HAR | 2022 | Deep transfer learning with graph neural network for sensor-based human activity recognition | 48 |
| ⬜ | CAPTURE-24 | 2024 | CAPTURE-24: A large dataset of wrist-worn activity tracker data collected in the wild for human activity recognition | 45 |
| ⬜ | PAR | 2021 | Context-aware support for cardiac health monitoring using federated machine learning | 12 |
| ⬜ | iSPL | 2022 | An Investigation on Deep Learning-Based Activity Recognition Using IMUs and Stretch Sensors | 11 |
| ⬜ | CHARM | 2021 | A recommendation specific human activity recognition dataset with mobile device's sensor data | 5 |
| ✅ | HARSense | 2021 | - | - |
| ⬜ | AReM | 2016 | - | - |