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

A machine learning study published in Gait & Posture investigated whether wearable sensor-derived gait metrics could reliably distinguish progressive supranuclear palsy (PSP) from Parkinson's disease (PD). Researchers identified clinically interpretable digital biomarkers from wearable gait analysis that differentiated the two conditions, which can be challenging to separate diagnostically. These findings may support physical therapists in objective movement assessment and monitoring of patients with atypical versus typical parkinsonism.

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