Human Balance Characterization for Parkinson's Disease Using COP Dynamics and Continuous, Adaptive and Explainable HMM-GNN Models

A system that characterises human balance for Parkinson's disease using COP dynamics and continuous, adaptive and explainable HMM-GNN models.

A system that characterises human balance for Parkinson's disease using COP dynamics and continuous, adaptive and explainable HMM-GNN models. This reference project walks through the whole job, from the problem and the design to the build and the evaluation, so that a developer can study how it works, run it and extend it.

It suits learners who want hands-on experience of the whole machine learning workflow: data collection and cleaning, feature engineering, model training and tuning, evaluation and presenting results.

The main parts are explanations of each result (feature importance or visual evidence), data preparation and exploration, model training and comparison of algorithms, evaluation with standard metrics, and a simple interface or API to try predictions. The reference build uses PyTorch Geometric, SHAP, Python, scikit-learn, and Pandas, with a clear project structure, setup instructions and sample data so that it can be run and extended.