ProsNet

A software package for developing classification models that predict physical behaviour postures.
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🤔 About The Project

This respository contains the sotware package and models described in the publication:

A Machine Learning Classification Model for Monitoring the Daily Physical Behaviour of Lower-Limb Amputees" (Griffiths et al., 2021).

The code works with data export from the activPAL activtiy monitor palt.com

Here are the main uses for this software:

Estimate physical behaviour postures from shank accelerometer data
Process shank accelerometer data along with thigh accelerometer event data to create a labeled dataset for training:
- Machine learning classifiers from heuristic features
- Deep learning classifiers from windowed acceleration data
Re-create the model development process used in Griffiths et al. (2021)
Experiment with new model development
Estimate non-wear periods from accelerometer data

See the example scripts for each of these use cases.

Built With

🚀 Getting Started

Test out the package and start processing data.

💻 Prerequisites

You need these pre-installed on your device to get started.

Python: A useful resource for installing python - instructions
Pipenv: A package management tool that automatically creates and manages a virtualenv for your projects, as well as adds/removes packages from your Pipfile as you install/uninstall packages. It also generates the ever-important Pipfile.lock, which is used to produce deterministic builds. This package can be installed using:

pip install pipenv

Installation

Open your terminal/shell and navigate to the directory where you want to install this software

Clone the repo

git clone https://github.com/Ben-Jamin-Griff/ProsNet.git

Move into repo
```
cd ProsNet
```
Install Python packages
```
pipenv install
```

🗺️ Exploring The Package

Make sure you completed the installation steps and then run the following command:

Unix/maxOS

python3 examples/shallow_examples/example_1.py

Windows

py examples\shallow_examples\example_1.py

This shows some of the basic functionality of the package. Look through the other examples or dive into the src folder to see what's happening under the hood.

🤝 Contributing

Contributions are what make the open source community such an amazing place. Any contributions you make are greatly appreciated.

Fork the Project
Create your Feature Branch (git checkout -b feature/AmazingFeature)
Commit your Changes (git commit -m 'Add some AmazingFeature')
Push to the Branch (git push origin feature/AmazingFeature)
Open a Pull Request

License

Distributed under the MIT License. See LICENSE for more information.

Author

👤 Benjamin Griffiths

sinnuswong / ProsNet