Baseline Machine Learning models for Human Activity Recognition (HAR) and Sleep Wakefulness Recognition (SWR) using the Human Activity Recognition Trondheim (HARTH), the Human Activity Recognition 70+ (HAR70+), the DualSleep, and the HARChildren 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, Validation of an Activity Type Recognition Model Classifying Daily Physical Behavior in Older Adults: The HAR70+ Model, A Machine Learning Model for Predicting Sleep and Wakefulness Based on Accelerometry, Skin Temperature and Contextual Information, and Validation of two novel human activity recognition models for typically developing children and children with Cerebral Palsy.
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 DualSleep dataset contains accelerometer recordings with sleep-stage annotations. It consists of acceleration data of 29 subjects, which wore two three-axial Axivity AX3 (Axivity Ltd., Newcastle, UK) accelerometers on the thigh and lower back. The dataset is available here. Download it for the sleep experiments. The config files for the DualSleep experiments are here.
- Acceleration signals
- 2 three-axial Axivity AX3 accelerometers
- Attached to: thigh and lower back
| Label | Activity | Notes |
|---|---|---|
| 81 | Wake | |
| 82 | Non-REM1 | |
| 83 | Non-REM2 | |
| 84 | Non-REM3 | |
| 85 | REM | |
| 86 | Movement | Body movements during sleep |
The HARChildren dataset contains activity annotations of 63 typically developing children and 16 children with Cerebral Palsy. The children were between 6 and 17 years old. Each wore two three-axial Axivity AX3 (Axivity Ltd., Newcastle, UK) accelerometers on the thigh and lower back. The dataset is available here. Download it for the HARChildren experiments. The config files for the HARChildren experiments are here.
- 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 | |
| 9 | transition | (not used in experiments) |
| 10 | bending | |
| 13 | cycling (sit) | |
| 14 | cycling (stand) | |
| 20 | jumping |
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/Start a LOSO using HARTH
cd experiments
./run_loocv.sh -c <path/to/model/config.yml> -d <path/to/dataset>
# Example: ./run_loocv.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/}
}If you use the DualSleep dataset for your research, please cite the following papers:
@article{logacjovMachineLearningModel2024,
title = {A {{Machine Learning Model}} for {{Predicting Sleep}} and {{Wakefulness Based}} on {{Accelerometry}}, {{Skin Temperature}} and {{Contextual Information}}},
author = {Logacjov, Aleksej and Skarpsno, Eivind Schjelderup and Kongsvold, Atle and Bach, Kerstin and Mork, Paul Jarle},
date = {2024-06-06},
journaltitle = {Nature and Science of Sleep},
shortjournal = {NSS},
volume = {16},
pages = {699--710},
publisher = {Dove Press},
doi = {10.2147/NSS.S452799}
}If you use the HARChildren dataset for your research, please cite the following papers:
@article{torringValidationTwoNovel2024,
title = {Validation of Two Novel Human Activity Recognition Models for Typically Developing Children and Children with {{Cerebral Palsy}}},
author = {Tørring, Marte Fossflaten and Logacjov, Aleksej and Brændvik, Siri Merete and Ustad, Astrid and Roeleveld, Karin and Bardal, Ellen Marie},
date = {2024-09-23},
journaltitle = {PLOS ONE},
shortjournal = {PLOS ONE},
volume = {19},
number = {9},
pages = {e0308853},
publisher = {Public Library of Science},
issn = {1932-6203},
doi = {10.1371/journal.pone.0308853}
}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
