用姿态估计分析临床视频，自动区分多种运动障碍。
Deep Learning Pose Estimation for Multi-Label Recognition of Combined Hyperkinetic Movement Disorders
- 基于姿态关键点提取运动特征，将视频转为可计算的时间序列
- 融合统计、时间、频域和复杂度特征，量化多种运动障碍表现
- 适合需要客观评估运动障碍的临床研究与长期监测场景
肌张力障碍、震颤、舞蹈症、肌阵挛和抽动等高动力性运动障碍（HMDs）在儿童及成人中具有致残性，其症状波动、间歇出现且常共现，导致临床识别和长期监测困难，目前仍依赖主观判断，易受不同医生间差异影响。本文提出一种基于姿态的机器学习框架，将常规门诊视频转换为解剖学有意义的关键点时间序列，并计算涵盖统计、时间、频谱及高阶不规则性-复杂性特征的运动学描述符，实现对重叠型HMD表型的客观区分。该方法为从日常临床视频中自动化识别和量化复杂运动障碍提供了新路径。
原文摘要 · Abstract (English)
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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