ProsNet 0.0.2

A package for processing activPAL activity monitor data.

ProsNet

A software package for developing classification models that predict physical behaviour postures.
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Table of Contents

1. About The Project
   • Built With
2. Getting Started
   • Prerequisites
   • Installation
3. Usage
4. Contributing
5. License
6. Author
7. Acknowledgements

🤔 About The Project

This respository contains the software and models developed in "A Machine Learning Classification Model for Monitoring the Daily Physical Behaviour of Lower-Limb Amputees" (preprint).

Here are the main uses of this software:

• Process shank activPAL accelerometer data to estimate daily physical behaviour postures
• Process shank activPAL accelerometer data with thigh activPAL event data for creating labeled datasets
• Re-create the model development process used in Griffiths et al. (2021)
• Experiment with new model development methods TBA
• Estimate non-wear periods from activPAL accelerometer data - algorithm validation ongoing

This repository is constantly being updated. Check back for more info...

Built With

• 🐍 Python
• Scikit-Learn

🚀 Getting Started

To get a local copy up and running follow these simple example steps.

💻 Prerequisites

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

• Python & pip: A useful resource for installing python - instructions

Installation

1. Open your terminal/shell and navigate to the directory where you want to install this software
2. Clone the repo

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

3. Move into repo

   cd ProsNet
   

4. Install Python packages

   pip install -r requirements.txt
   

Usage

Make sure you have cloned the repository and installed requirements.txt

Just run the following command at the root of your project:

python3 examples/

🤝 Contributing

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

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

License

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

Author

👤 Benjamin Griffiths

• Twitter
• Github

Acknowledgements

TBC