HAR Datasets and Machine Learning experiments

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.

HARTH Dataset

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.

Setup

• Acceleration signals
• 2 three-axial Axivity AX3 accelerometers
• Attached to: thigh and lower back

Activity Annotations

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

HAR70+ Dataset

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.

Setup

• Acceleration signals
• 2 three-axial Axivity AX3 accelerometers
• Attached to: thigh and lower back

Activity Annotations

Label	Activity	Notes
1	walking
3	shuffling	standing with leg movement
4	stairs (ascending)
5	stairs (descending)
6	standing
7	sitting
8	lying

Machine Learning Experiments

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.

Requirements

• Python 3.8.10+

cd experiments
pip install -r requirements.txt

Usage

Start 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

Citation

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/}
}

Note

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