Baseline Machine Learning models for the Human Activity Recognition Trondheim (HARTH) and the Human Acceleration Recognition 70+ (HAR70+) datasets, proposed and used in our papers: HARTH: A Human Activity Recognition Dataset for Machine Learning, A Machine Learning Classifier for Detection of Physical Activity Types and Postures During Free-Living, and Validation of an Activity Type Recognition Model Classifying Daily Physical Behavior in Older Adults: The HAR70+ Model.
The folder harth contains the Human Activity Recognition Trondheim Dataset (HARTH). It consists of acceleration data of 22 subjects, which wore two three-axial Axivity AX3 (Axivity Ltd., Newcastle, UK) accelerometers on the thigh and lower back. The dataset is also uploaded to the UC Irvine Machine Learning Repository.
- Acceleration signals
- 2 three-axial Axivity AX3 accelerometers
- Attached to: thigh and lower back
| Label | Activity | Notes |
|---|---|---|
| 1 | walking | |
| 2 | running | |
| 3 | shuffling | standing with leg movement |
| 4 | stairs (ascending) | |
| 5 | stairs (descending) | |
| 6 | standing | |
| 7 | sitting | |
| 8 | lying | |
| 13 | cycling (sit) | |
| 14 | cycling (stand) | |
| 130 | cycling (sit, inactive) | cycling (sit) without leg movement |
| 140 | cycling (stand, inactive) | cycling (stand) without leg movement |
The folder har70plus contains the Human Activity Recognition 70+ (HAR70+) dataset. It consists of acceleration data of 18 older-adult subjects, which wore two three-axial Axivity AX3 (Axivity Ltd., Newcastle, UK) accelerometers on the thigh and lower back. The dataset is also uploaded to the UC Irvine Machine Learning Repository.
- Acceleration signals
- 2 three-axial Axivity AX3 accelerometers
- Attached to: thigh and lower back
| Label | Activity | Notes |
|---|---|---|
| 1 | walking | |
| 3 | shuffling | standing with leg movement |
| 4 | stairs (ascending) | |
| 5 | stairs (descending) | |
| 6 | standing | |
| 7 | sitting | |
| 8 | lying |
The folder experiments contains all our experiments. It is possible to train a K-Nearest Neighbors, a Support Vector Machine, a Random Forest, an Extreme Gradient Boost, a Convolutional Neural Network, a Bidirectional Long Short-term Memory, and a CNN with multi-resolution blocks.
- Python 3.8.10+
cd experiments
pip install -r requirements.txtStart a model training using HARTH
cd experiments
./run_training.sh -c <path/to/model/config.yml> -d <path/to/dataset>
# Example: ./run_training.sh -c traditional_machine_learning/params/xgb_50hz/config.yml -d ../harth/Each model can be configured using the corresponding config.yml file: xgb, svm, rf, knn, cnn, multi_resolution_cnn, lstm
If you use the HARTH dataset for your research, please cite the following papers:
@article{logacjovHARTHHumanActivity2021,
title = {{{HARTH}}: {{A Human Activity Recognition Dataset}} for {{Machine Learning}}},
shorttitle = {{{HARTH}}},
author = {Logacjov, Aleksej and Bach, Kerstin and Kongsvold, Atle and B{\aa}rdstu, Hilde Bremseth and Mork, Paul Jarle},
year = {2021},
month = nov,
journal = {Sensors},
volume = {21},
number = {23},
pages = {7853},
publisher = {{Multidisciplinary Digital Publishing Institute}},
doi = {10.3390/s21237853}
}@article{bachMachineLearningClassifier2021,
title = {A {{Machine Learning Classifier}} for {{Detection}} of {{Physical Activity Types}} and {{Postures During Free-Living}}},
author = {Bach, Kerstin and Kongsvold, Atle and B{\aa}rdstu, Hilde and Bardal, Ellen Marie and Kj{\ae}rnli, H{\aa}kon S. and Herland, Sverre and Logacjov, Aleksej and Mork, Paul Jarle},
year = {2021},
month = dec,
journal = {Journal for the Measurement of Physical Behaviour},
pages = {1--8},
publisher = {{Human Kinetics}},
doi = {10.1123/jmpb.2021-0015},
}If you use the HAR70+ dataset for your research, please cite the following paper:
@article{ustadValidationActivityType2023,
title = {Validation of an {{Activity Type Recognition Model Classifying Daily Physical Behavior}} in {{Older Adults}}: {{The HAR70}}+ {{Model}}},
shorttitle = {Validation of an {{Activity Type Recognition Model Classifying Daily Physical Behavior}} in {{Older Adults}}},
author = {Ustad, Astrid and Logacjov, Aleksej and Trolleb{\o}, Stine {\O}verengen and Thingstad, Pernille and Vereijken, Beatrix and Bach, Kerstin and Maroni, Nina Skj{\ae}ret},
year = {2023},
month = jan,
journal = {Sensors},
volume = {23},
number = {5},
pages = {2368},
publisher = {{Multidisciplinary Digital Publishing Institute}},
issn = {1424-8220},
doi = {10.3390/s23052368},
copyright = {http://creativecommons.org/licenses/by/3.0/}
}Our HARTH dataset is subject to changes in future releases. Therefore, consider version v1.0 for reproducibility purposes. It contains the dataset and experiments used in our article, HARTH: A Human Activity Recognition Dataset for Machine Learning