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

• URL: http://arxiv.org/abs/2602.00163v1
• Date: Thu, 29 Jan 2026 21:55:48 GMT
• Title: Deep Learning Pose Estimation for Multi-Label Recognition of Combined Hyperkinetic Movement Disorders
• Authors: 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,
• Abstract summary: Hyperkinetic movement disorders (HMDs) such as dystonia, tremor, chorea, myoclonus, and tics are disabling motor manifestations across childhood and adulthood.<n>Here, we developed a pose-based machine-learning framework that converts standard outpatient videos into anatomically meaningful keypoint time series.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Detecting Autism Spectrum Disorder with Deep Eye Movement Features [9.225838905985958]
  Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by deficits in social communication and behavioral patterns.<n>Eye movement data offers a non-invasive diagnostic tool for ASD detection.<n>To efficiently capture subtle and complex eye movement patterns, a discrete short-term sequential (DSTS) modeling framework is designed.
  arXiv  Detail & Related papers  (2026-01-09T14:35:24Z)
• TARDis: Time Attenuated Representation Disentanglement for Incomplete Multi-Modal Tumor Segmentation and Classification [10.329406702659123]
  Tumor segmentation and diagnosis in contrast-enhanced Computed Tomography (CT) rely heavily on the physiological dynamics of contrast agents.<n>Existing deep learning approaches typically treat missing phases as absent independent channels, ignoring the inherent temporal continuity of hemodynamics.<n>We propose Time Attenuated Representation Disentanglement (TARDis), a novel physics-aware framework that redefines missing modalities as missing sample points on a continuous Time-Attenuation Curve.
  arXiv  Detail & Related papers  (2025-12-04T08:44:50Z)
• Conditional Neural ODE for Longitudinal Parkinson's Disease Progression Forecasting [51.906871559732245]
  Parkinson's disease (PD) shows heterogeneous, evolving brain-morphometry patterns.<n>Modeling these longitudinal trajectories enables mechanistic insight, treatment development, and individualized 'digital-twin' forecasting.<n>We propose CNODE, a novel framework for continuous, individualized PD progression forecasting.
  arXiv  Detail & Related papers  (2025-11-06T20:16:33Z)
• Generalizable automated ischaemic stroke lesion segmentation with vision transformers [0.7400397057238803]
  Diffusion-weighted imaging (DWI) provides the highest expressivity in ischemic stroke.<n>Current U-Net-based models therefore underperform, a problem accentuated by inadequate evaluation metrics.<n>Here, we present a high-performance DWI lesion segmentation tool addressing these challenges.
  arXiv  Detail & Related papers  (2025-02-10T19:00:00Z)
• MVICAD2: Multi-View Independent Component Analysis with Delays and Dilations [61.59658203704757]
  We propose Multi-View Independent Component Analysis with Delays and Dilations (MVICAD2), which allows sources to differ across subjects in both temporal delays and dilations.<n>We present a model with identifiable sources, derive an approximation of its likelihood in closed form, and use regularization and optimization techniques to enhance performance.
  arXiv  Detail & Related papers  (2025-01-13T15:47:02Z)
• Deep Learning-Based Classification of Hyperkinetic Movement Disorders in Children [0.0]
  Hyperkinetic movement disorders (HMDs) in children pose significant diagnostic challenges due to overlapping clinical features. This study develops a neural network model to differentiate between dystonia and chorea from video recordings of paediatric patients performing motor tasks.
  arXiv  Detail & Related papers  (2024-11-19T22:02:04Z)
• CTPD: Cross-Modal Temporal Pattern Discovery for Enhanced Multimodal Electronic Health Records Analysis [50.56875995511431]
  We introduce a Cross-Modal Temporal Pattern Discovery (CTPD) framework, designed to efficiently extract meaningful cross-modal temporal patterns from multimodal EHR data.<n>Our approach introduces shared initial temporal pattern representations which are refined using slot attention to generate temporal semantic embeddings.
  arXiv  Detail & Related papers  (2024-11-01T15:54:07Z)
• Individualized Dosing Dynamics via Neural Eigen Decomposition [51.62933814971523]
  We introduce the Neural Eigen Differential Equation algorithm (NESDE) NESDE provides individualized modeling, tunable generalization to new treatment policies, and fast, continuous, closed-form prediction. We demonstrate the robustness of NESDE in both synthetic and real medical problems, and use the learned dynamics to publish simulated medical gym environments.
  arXiv  Detail & Related papers  (2023-06-24T17:01:51Z)
• Estimating Motor Symptom Presence and Severity in Parkinson's Disease from Wrist Accelerometer Time Series using ROCKET and InceptionTime [4.1125736844411644]
  We investigate InceptionTime and RandOm Convolutional KErnel Transform as promising for Parkinson's symptom monitoring.<n>InceptionTime's high learning capacity is well-suited to modeling complex movement patterns, while ROCKET is suited to small datasets.<n>Our findings indicate that all approaches can learn to estimate tremor severity and bradykinesia presence with moderate performance but encounter challenges in detecting dyskinesia.
  arXiv  Detail & Related papers  (2023-04-21T22:38:44Z)
• Non-contact Pain Recognition from Video Sequences with Remote Physiological Measurements Prediction [53.03469655641418]
  We present a novel multi-task learning framework which encodes both appearance changes and physiological cues in a non-contact manner for pain recognition. We establish the state-of-the-art performance of non-contact pain recognition on publicly available pain databases.
  arXiv  Detail & Related papers  (2021-05-18T20:47:45Z)
• Learning Dynamic and Personalized Comorbidity Networks from Event Data using Deep Diffusion Processes [102.02672176520382]
  Comorbid diseases co-occur and progress via complex temporal patterns that vary among individuals. In electronic health records we can observe the different diseases a patient has, but can only infer the temporal relationship between each co-morbid condition. We develop deep diffusion processes to model "dynamic comorbidity networks"
  arXiv  Detail & Related papers  (2020-01-08T15:47:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.