Authors: Daniel Deng, Jill L. Ostrem, Vy Nguyen, Daniel D. Cummins, Julia Sun, Anupam Pathak, Simon Little, Reza Abbasi-Asl
Manuscript: https://www.medrxiv.org/content/10.1101/2023.11.04.23298083v1
In the root directory, install Python dependencies by run the command
pip install -r requirements.txt./modules/tracking.py: classes for parsing and preprocessing demographic data, UPDRS data, and MediaPipe landmark timeseries data
./modules/feature_extraction.py: defines temporal and spectral features to be extracted from landmark timeseries
./modules/cross_validation.py: custom leave-one-subject-out cross-validation (CV) for selecting LASSO feature selection and model parameters
./modules/model_selection.py: training and validation of models; aggregation of model results
./modules/visualize.py: visualization methods for datasets and CV results
./modules/utils.py: miscellaneous helper methods
MediaPipe (https://developers.google.com/mediapipe) should first be used to extract kinematic landmarks as time series. The resultant .csv files should be placed under ./dataset. Currently, only hand landmark timeseries and pose landmark timeseries are supported by the software. Code for parsing additional modalities should extend the LandmarkSeries class in ./modules/tracking.py.
To run the analysis, follow the code instructions in ./analysis.ipynb. By default, extracted features and validation results are stored under ./cache.
Last update: Sept 2023, v1.0
Daniel Deng, Jill L. Ostrem, Vy Nguyen, Daniel D. Cummins, Julia Sun, Anupam Pathak, Simon Little, Reza Abbasi-Asl. Interpretable Video-Based Tracking and Quantification of Parkinsonism Clinical Motor States. medRxiv 2023.11.04.23298083; doi: https://doi.org/10.1101/2023.11.04.23298083