Glenohumeral Joint Force Prediction with Deep Learning

This repository reflects the data and code of the study "Glenohumeral Joint Force Prediction with Deep Learning". The objective of the study was to predict glenohumeral joint force (the reaction force on the glenoid) with deep learning.

Getting Started

The following instructions will get you a copy of the project locally and help you to train the model or perform predictions with it.

• Clone/download the repository

You might use the code with google colab or locally with an installed jupyter (lab or notebook). In the case of google colab do as follows

• Open google colab through https://colab.research.google.com/
• Upload the MSM_DLM.ipynb from the cloned/downloaded repository directory to the google colab through Upload -> Choose File.
• Upload the "data_for_model.csv" and "model.h5" through left panel Files -> Upload to session storage. If you want to perform only training then just upload "data_for_model.csv" and at the end of the training you will have a "model.h5". If you want to perform only predictions then just upload the "model.h5".
• Follow the instructions in the notebook to train and evaluate the model or perform predictions with it.

In the case of using jupyter locally do as follows

• Install the libraries.

https://www.tensorflow.org/install https://scikit-learn.org/stable/install.html https://keras.io/keras_tuner/

• Open jupyter in the cloned/downloaded repository directory.
• Open the MSM_DLM.ipynb notebook file with jupyter.
• Follow the instructions in the notebook to train and evaluate the model or perform prediction with it.

Prerequisites

The model is coded with Python 3.10 and tensorflow 2.12.0.

• 1. Authors
• Pezhman Eghbalishamsabadi (EPFL-LBO).

• 1. Contributors
• Léa Pistorius (EPFL-LBO): performed first automatic simulation of the virtual subjects and developed a machine learning model to predict the glenohumeral joint force magnitude.
• Alexandre Terrier (EPFL-LBO): biomechanical supervision

License

This should be discussed with Technology Transfer Office (TTO) of EPFL

Acknowledgments

The project was financially supported by the Swiss National Science Foundation (SNSF)

• SNF 189972