Fig. 1: Schematic overview of automatic feature extraction from video recordings for classification of Parkinson’s disease motor symptom severity.

a From recordings of participants performing prescribed motor assessment tasks, we extracted movement (kinematic) time series at key landmarks using the pose estimation library MediaPipe. Relative movement measurements were computed based on the extracted signals, from which various temporal and spectral metrics were computed as features. Pairwise combinations of samples from the same patient under the same medication state were performed on the body and hand feature sets to form the bi-modality combination feature set. b To obtain objective measurements of classification performances, we performed a leave-one-subject-out cross-validation (CV), where samples from one patient are held out as the training set for each CV iteration, on each dataset. The CV process is repeated 16 times to account for variabilities in models trained. During CV, a small subset of features with high predictive power with regard to the assigned group labels was selected via least absolute shrinkage and selection operator (LASSO) feature selection based on the training set. The fitted feature selection is then applied to both the training and validation sets. The selected features of the training and validation sets were used to train and validate, respectively, the various machine learning (ML) models. c LASSO feature selection consistently identified small sets of salient features during each CV iteration. The average number of features selected at each iteration is reported as mean ± standard deviation.