ProsNet 0.0.5

A package for processing activPAL activity monitor data.

ProsNet

A software package for developing classification models that predict physical behaviour postures.
Explore the docs »

🤔 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" (currently under review).

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

• 🐍 Python
• Scikit-Learn
• TensorFlow

🚀 Getting Started

Test out the package and start processing data.

💻 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 ProsNet
   

Usage

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

🗺️ Exploring The Package

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

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
   

🤝 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

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