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arXiv · 2602.00163

Deep Learning Pose Estimation for Multi-Label Recognition of Combined Hyperkinetic Movement Disorders

Laura Cif·Diane Demailly·Gabriella A. Horvàth·Juan Dario Ortigoza Escobar·Nathalie Dorison·Mayté Castro Jiménez·Cécile A. Hubsch·Thomas Wirth·Gun-Marie Hariz·Sophie Huby·Morgan Dornadic·Zohra Souei·Muhammad Mushhood Ur Rehman·Simone Hemm·Mehdi Boulayme·Eduardo M. Moraud·Jocelyne Bloch·Xavier Vasques
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Abstract

Hyperkinetic movement disorders (HMDs) such as dystonia, tremor, chorea, myoclonus, and tics are disabling motor manifestations across childhood and adulthood. Their fluctuating, intermittent, and frequently co-occurring expressions hinder clinical recognition and longitudinal monitoring, which remain largely subjective and vulnerable to inter-rater variability. Objective and scalable methods to distinguish overlapping HMD phenotypes from routine clinical videos are still lacking. Here, we developed a pose-based machine-learning framework that converts standard outpatient videos into anatomically meaningful keypoint time series and computes kinematic descriptors spanning statistical, temporal, spectral, and higher-order irregularity-complexity features.

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Laura Cif, Diane Demailly, Gabriella A. Horvàth, Juan Dario Ortigoza Escobar, Nathalie Dorison, Mayté Castro Jiménez, Cécile A. Hubsch, Thomas Wirth, Gun-Marie Hariz, Sophie Huby, Morgan Dornadic, Zohra Souei, Muhammad Mushhood Ur Rehman, Simone Hemm, Mehdi Boulayme, Eduardo M. Moraud, Jocelyne Bloch, Xavier Vasques. 2026-01-29. Deep Learning Pose Estimation for Multi-Label Recognition of Combined Hyperkinetic Movement Disorders. https://doi.org/10.1002/acn3.70474

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