Weighted-ShapeDBA-4-Rehab

This is the code of our paper "Weighted Average of Human Motion Sequences for Improving Rehabilitation Assessment" accepted at the 9th International Workshop on Advanced Analytics and Learning on Temporal Data, at ECML-PKDD 2024
This work was done by Ali Ismail-Fawaz, Maxime Devanne, Stefano Berretti, Jonathan Weber and Germain Forestier.

Requirements

tensorflow
numpy
hydra-core
aeon
scikit-learn
matplotlib

Usage

Create the extended dataset

This code utilizes the hydra configuration setup, simply edit the parameters of the configuration file ('config/config_hydra.yaml`) and run the following:

python main_data_extension.py

It will create the base dataset and extend it using a noisy version (baseline) as well as using the weighted shape DBA for various number of neighbors. The different parameters include:

• original_dir: The directory where the original Kimore dataset is stored
• res_directory: The output directory where the results are stored
• exercise: The exercise to consider
• num_folds: The number of folds per exercise to consider
• num_neighbors_wsdba: The list of number of neighbors used for weighted shape DBA

Train and evaluate a Regressor

This code utilizes the hydra configuration setup, simply edit the parameters of the configuration file ('config/config_hydra.yaml`) and run the following:

python main_regression.py

It creates and trains and avaluates a regressor for all version of the dataset (original + extended version). For now, only the FCN regressor is available. The different parameters include:

• res_directory: The output directory where the results are stored
• exercise: The exercise to consider
• num_folds: The number of folds per exercise to consider
• num_neighbors_wsdba: The list of number of neighbors used for weighted shape DBA
• nb_epochs: Number of epochs to train the regressor
• batch_size: The batch size to use in the regressor