  This study investigates the application of general human motion encoders
trained on large-scale human motion datasets for analyzing gait patterns in PD
patients. Although these models have learned a wealth of human biomechanical
knowledge, their effectiveness in analyzing pathological movements, such as
parkinsonian gait, has yet to be fully validated. We propose a comparative
framework and evaluate six pre-trained state-of-the-art human motion encoder
models on their ability to predict the Movement Disorder Society - Unified
Parkinson's Disease Rating Scale (MDS-UPDRS-III) gait scores from motion
capture data. We compare these against a traditional gait feature-based
predictive model in a recently released large public PD dataset, including PD
patients on and off medication. The feature-based model currently shows higher
weighted average accuracy, precision, recall, and F1-score. Motion encoder
models with closely comparable results demonstrate promise for scalability and
efficiency in clinical settings. This potential is underscored by the enhanced
performance of the encoder model upon fine-tuning on PD training set. Four of
the six human motion models examined provided prediction scores that were
significantly different between on- and off-medication states. This finding
reveals the sensitivity of motion encoder models to nuanced clinical changes.
It also underscores the necessity for continued customization of these models
to better capture disease-specific features, thereby reducing the reliance on
labor-intensive feature engineering. Lastly, we establish a benchmark for the
analysis of skeleton-based motion encoder models in clinical settings. To the
best of our knowledge, this is the first study to provide a benchmark that
enables state-of-the-art models to be tested and compete in a clinical context.
Codes and benchmark leaderboard are available at code.
