用无标记动作捕捉提升临床肢体评估的客观性与敏感度
Markerless Motion Capture in Routine Clinical Upper Limb Assessments: Validity and Insights Beyond Ordinal Scoring
- 将AI无标记动作捕捉嵌入常规评估流程
- 1174次任务中运动重建准确，能区分不同损伤程度
- 可发现评分相同但恢复模式不同的患者差异
动作研究手臂测试（ARAT）是神经康复中广泛使用的上肢功能评估工具，但其等级评分主观性强，灵敏度和特异性有限。本研究评估了在临床常规中嵌入人工智能驱动的无标记动作捕捉（MMC）是否能准确重建上肢运动，并生成具有临床意义的客观运动学指标，超越传统等级评分。基于20名混合神经系统疾病患者共47次会话（1,174个ARAT任务）的数据，生物力学重建在不同损伤水平下均表现准确且稳健，运动学指标展现出预期的构念效度区分模式。纵向案例分析表明，该方法弥补了等级评分的不足：领域分解揭示了相同ARAT得分下患者特有的恢复轨迹（特异性），且在ARAT评分已达天花板后仍能检测到运动改善（灵敏度）。因此，临床常规中应用无标记动作捕捉可提供有效、客观、敏感且特异的运动学测量，补充传统评分。
原文摘要 · Abstract (English)
The Action Research Arm Test (ARAT) is a widely-used upper limb outcome measure in neurorehabilitation, but its ordinal scoring is subjective and suffers from limited sensitivity and specificity. We evaluated whether artificial-intelligence (AI)-based markerless motion capture (MMC), embedded into ARAT assessments during clinical routine, accurately reconstructs upper limb movement and yields valid, objective kinematic metrics carrying clinically meaningful information beyond the ordinal score. Across 47 sessions from 20 mixed-neurological patients (1,174 ARAT tasks), biomechanical reconstruction was accurate and robust across impairment levels, and kinematic metrics showed the discrimination pattern expected of a construct-valid measure. In longitudinal case studies, the metrics added the specificity and sensitivity the ordinal score lacks: a domain decomposition exposed patient-specific recovery profiles underlying equal ARAT gains (specificity), and kinematic improvement continued to be detected after the ARAT had saturated (sensitivity). MMC in clinical routine can thus provide valid, objective, sensitive, and specific kinematic measurement complementing ordinal scoring.
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