• Xiaozhou Ye, Email: [email protected]
• Kevin Wang, Email: [email protected]

Department of Electrical, Computer, and Software Engineering, The University of Auckland

This repository contains the code and data for our paper: "Deep Generative Domain Adaptation with Temporal Relation Attention Mechanism for Cross-User Activity Recognition". The goal of this project is to enhance cross-user Human Activity Recognition (HAR) by integrating temporal dependency relations during domain adaptation.

[https://www.sciencedirect.com/science/article/pii/S0031320324005624]

@article{ye2024deep, title={Deep generative domain adaptation with temporal relation attention mechanism for cross-user activity recognition}, author={Ye, Xiaozhou and Kevin, I and Wang, Kai}, journal={Pattern Recognition}, pages={110811}, year={2024}, publisher={Elsevier} }

Python 3.10.11

pip install -r requirements.txt

asttokens==2.4.1 colorama==0.4.6 comm==0.2.2 contourpy==1.2.0 cycler==0.12.1 decorator==5.1.1 exceptiongroup==1.2.1 executing==2.0.1 filelock==3.14.0 fonttools==4.50.0 fsspec==2024.5.0 giotto-ph==0.2.2 giotto-tda==0.6.0 igraph==0.11.5 intel-openmp==2021.4.0 ipython==8.24.0 ipywidgets==8.1.2 jedi==0.19.1 Jinja2==3.1.4 joblib==1.4.2 jupyterlab_widgets==3.0.10 kiwisolver==1.4.5 MarkupSafe==2.1.5 matplotlib==3.8.3 matplotlib-inline==0.1.7 mkl==2021.4.0 mpmath==1.3.0 networkx==3.3 numpy==1.26.4 packaging==24.0 pandas==2.2.1 parso==0.8.4 patsy==0.5.6 pillow==10.2.0 plotly==5.22.0 POT==0.9.3 prompt-toolkit==3.0.43 pure-eval==0.2.2 pyflagser==0.4.5 Pygments==2.18.0 pyparsing==3.1.2 python-dateutil==2.9.0.post0 pytz==2024.1 scikit-learn==1.4.2 scipy==1.13.0 six==1.16.0 stack-data==0.6.3 statsmodels==0.14.2 sympy==1.12 tbb==2021.12.0 tenacity==8.3.0 texttable==1.7.0 threadpoolctl==3.5.0 torch==2.3.0 traitlets==5.14.3 typing_extensions==4.11.0 tzdata==2024.1 wcwidth==0.2.13 widgetsnbextension==4.0.10

The datasets used in this project are OPPT, PAMAP2, and DSADS. Download the datasets from their respective sources.

http://www.opportunity-project.eu/node/56.html

https://archive.ics.uci.edu/dataset/231/pamap2+physical+activity+monitoring

https://archive.ics.uci.edu/dataset/256/daily+and+sports+activities

This repository contains various scripts and modules essential for training and running a deep generative temporal relation model with autoregression across different datasets (DSADS, OPPT, and PAMAP2).

• GPU_DSADS_deepGenTRNet_main_with_autoregression.py: Main script for training the model using the DSADS dataset.
• GPU_OPPT_deepGenTRNet_main_with_autoregression.py: Main script for training the model using the OPPT dataset.
• GPU_PAMAP2_deepGenTRNet_main_with_autoregression.py: Main script for training the model using the PAMAP2 dataset.

• utils.py: Contains helper functions for data processing and visualization.

• gen_model/train.py: Script for the training phase of deep learning models.

• gen_model/alg/DeepGenTempRelaNet.py: Defines the deep generative temporal relation model class.
• gen_model/alg/linear_regression.py: Contains the definition and training function for a linear regression model.
• gen_model/alg/modelopera.py: Utility module for various model operations.
• gen_model/alg/opt.py: Includes functions for obtaining and setting the optimizer.

• gen_model/loss/common_loss.py: Implements various loss functions used within the models.

• gen_model/network/Adver_network.py: Defines a neural network model including a backpropagation function and a discriminator model.
• gen_model/network/common_network.py: Contains definitions for common neural network models.
• gen_model/network/feature_extraction_network.py: Definitions for feature extraction models in neural networks.

• gen_model/utils/util.py: Provides helper functions for logging, formatting, and setting random seeds.

• OPPT_get_features_samples.py: reads the OPPT dataset, selects specific activities and sensor channels, and extracts data for a source user and a target user

• PAMAP2_get_features_samples.py: reads the PAMAP2 dataset, selects specific activities and sensor channels, and extracts data for a source user and a target user

• DSADS_get_features_samples.py: reads the DSADS dataset, selects specific activities and sensor channels, and extracts data for a source user and a target user

• read_dataset/read_OPPT_dataset.py: read and process OPPT data. It initializes with parameters for users, window sizes, and sampling frequency, and includes methods to map activities, identify sensor channels, and segment data

• read_dataset/read_PAMAP2_dataset.py: read and process PAMAP2 data. It initializes with parameters for users, window sizes, and sampling frequency, and includes methods to map activities, identify sensor channels, and segment data

• read_dataset/read_DSADS_dataset.py: read and process DSADS data. It initializes with parameters for users, window sizes, and sampling frequency, and includes methods to map activities, identify sensor channels, and segment data

The hyperparameters used in the model can significantly impact performance. Here are the key hyperparameters and their default values:

Here is a basic example to get you started with training the model on the OPPT dataset:

python GPU_OPPT_deepGenTRNet_main_with_autoregression.py

This script will load the OPPT dataset, initialize the model, and start training.