Home-based measurements of dystonia and choreoathetosis in cerebral palsy using smartphone-coupled inertial sensor technology and machine learning: A proof-of-concept study - dataset

Home-based measurements of dystonia in cerebral palsy using smartphone-coupled inertial sensor technology and machine learning: A proof-of-concept study This project contains: - 1 main MATLAB script: MODYSathome_main.m - 12 MATLAB functions: - function_calc_mean_recall_precision.m - function_create_dataframes.m - function_deep_learning.m - function_determine_best_ML_model.m - function_display_DL_results.m - function_display_ML_results.m - function_index_extremities.m - function_machine_learning.m - function_oversample.m - function_partition_data.m - function_pick_best_models.m - function_prepare_DL_data.m Downloading the Matlab scripts - Create a folder named 'MODYS' and create a subfolder named 'results' - Download the zip file via RehabAUmc/modys-at-home: v1.0 | Zenodo - Unzip the zip file in the path MODYS\ STEPS 1. Open MATLAB 2. In MATLAB, go to the 'HOME' tab and click on 'Set Path' 3. Click on 'Add Folder' and browse to MODYS/RehabAUmc-modys-at-home-86b14c3/functions 4. Click on 'Select Folder' and click on 'Save' 5. Click on 'Browse to folder' and browse to a patients' data in MODYS/data/PatientXXX, then click on 'Select Folder' 6. In the 'HOME' tab click on 'Open' and open MODYSathome.m in MODYS/RehabAUmc-modys-at-home-86b14c3 7. In the 'EDITOR' tab click on 'Run Section' to run the script 8. When the code has been run, the results are displayed in the Command Window and saved in MODYS/results/PatientXXX