Wearable gait analysis for differentiating progressive supranuclear palsy and Parkinson's disease: Clinically interpretable digital biomarkers using machine learning.

Early differentiation of progressive supranuclear palsy from Parkinson's disease is challenging due to overlapping motor features, particularly in early disease stages where clinical misclassification is common.

To evaluate whether sensor-derived gait measures can distinguish progressive supranuclear palsy from Parkinson's disease using machine learning, and to compare performance between raw time-series signals and aggregate spatiotemporal variables, with emphasis on clinically interpretable gait biomarkers.