Peer-Reviewed Publication
Gait Posture2026;130110610.August 3, 2026Journal Article
Wearable gait analysis for differentiating progressive supranuclear palsy and Parkinson's disease: Clinically interpretable digital biomarkers using machine learning.
Maryam Sadeghi1, Ehsan Barouti2, Kelly E Lyons3, Rajesh Pahwa3, Abiodun Akinwuntan4, Jeffrey Thompson5, Sanghee Moon6, Hannes Devos7
1Department of Physical Therapy, Rehabilitation Science, and Athletic Training, University of Kansas Medical Center (KUMC), Kansas City, KS, USA. Electronic address: sadeghim@ohsu.edu.
2Department of Computer Engineering, Amirkabir University of Technology, Tehran, Iran.
3Department of Neurology, University of Kansas Medical Center, Kansas City, KS, USA.
4Department of Physical Therapy, Rehabilitation Science, and Athletic Training, University of Kansas Medical Center (KUMC), Kansas City, KS, USA; Office of the Dean, School of Health Professions, University of Kansas Medical Center, Kansas City, KS, USA.
5Department of Biostatistics, University of Kansas Medical Center, Kansas City, KS, USA.
6Department of Kinesiology, University of New Hampshire, Durham, NH, USA.
7Department of Physical Therapy, Rehabilitation Science, and Athletic Training, University of Kansas Medical Center (KUMC), Kansas City, KS, USA.
Abstract
BACKGROUND: 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. Wearable inertial sensors provide high-resolution gait data that may reveal disease-specific and clinically relevant signatures.
OBJECTIVE: To evaluate whether sensor-derived…
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