Deep Learning Pose Estimation for Phenotyping of Co‐Occurring Hyperkinetic Movement Disorders
ABSTRACT Objective To explore whether routine outpatient video combined with deep learning‐based pose estimation and clinically interpretable kinematic features can support multi‐label phenotyping of co‐occurring hyperkinetic movement disorders (HMDs).
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Advances in Computational and Experimental Approaches to Chemical Short-Range Order Formation in Multi-Principal Element Alloys. [PDF]
Asle Zaeem M, Liaw PK.
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Statistical damage model of rock considering pore water pressure and multifractal characteristics. [PDF]
Zhang Y, Li F.
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Obsessive-compulsive tendencies shift the balance between competitive neurocognitive functions. [PDF]
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Electronegativity-Inspired Multidimensional and Complex Representations for Modeling Chemical Environments in Materials. [PDF]
An Y, Jeon S, Han DB, Kim HW.
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Effects of size and atomic defects on the buckling of single-layer and double-layer graphene nanoplates using AFEM. [PDF]
Aliakbari K, Jafari A, Amiri M.
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Flexible ultrasound array for subcortical brain stimulation in humans: a simulation study. [PDF]
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