标题： 深度学习姿态估计用于联合高动症运动障碍的多标签识别 显示英文标题

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

摘要： 肌张力障碍、震颤、舞蹈症、肌阵挛和抽动等高活动性运动障碍（HMDs）是在儿童和成人中出现的致残性运动表现。它们的波动性、间歇性和经常同时出现的表现妨碍了临床识别和长期监测，这仍然主要是主观的，并且容易受到评估者之间差异的影响。从常规临床视频中区分重叠的HMD表型的客观且可扩展的方法仍然缺乏。在这里，我们开发了一个基于姿态的机器学习框架，将标准门诊视频转换为解剖上有意义的关键点时间序列，并计算涵盖统计、时间、频谱和高阶不规则性-复杂性特征的运动学描述符。 显示英文摘要

摘要： 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.