Deep Learning Pose Estimation for Multi-Label Recognition of Combined Hyperkinetic Movement Disorders
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.
Cite
@article{arxiv.2602.00163,
title = {Deep Learning Pose Estimation for Multi-Label Recognition of Combined Hyperkinetic Movement Disorders},
author = {Laura Cif and Diane Demailly and Gabriella A. Horvàth and Juan Dario Ortigoza Escobar and Nathalie Dorison and Mayté Castro Jiménez and Cécile A. Hubsch and Thomas Wirth and Gun-Marie Hariz and Sophie Huby and Morgan Dornadic and Zohra Souei and Muhammad Mushhood Ur Rehman and Simone Hemm and Mehdi Boulayme and Eduardo M. Moraud and Jocelyne Bloch and Xavier Vasques},
journal= {arXiv preprint arXiv:2602.00163},
year = {2026}
}