Marker-based Optical Motion Capture (OMC) paired with biomechanical modeling is currently considered the most precise and accurate method for measuring human movement kinematics. However, combining differentiable biomechanical modeling with Markerless Motion Capture (MMC) offers a promising approach to motion capture in clinical settings, requiring only minimal equipment, such as synchronized webcams, and minimal effort for data collection. This study compares key kinematic outcomes from biomechanically modeled MMC and OMC data in 15 stroke patients performing the drinking task, a functional task recommended for assessing upper limb movement quality. We observed a high level of agreement in kinematic trajectories between MMC and OMC, as indicated by high correlations (median r above 0.95 for the majority of kinematic trajectories) and median RMSE values ranging from 2-5 degrees for joint angles, 0.04 m/s for end-effector velocity, and 6 mm for trunk displacement. Trial-to-trial biases between OMC and MMC were consistent within participant sessions, with interquartile ranges of bias around 1-3 degrees for joint angles, 0.01 m/s in end-effector velocity, and approximately 3mm for trunk displacement. Our findings indicate that our MMC for arm tracking is approaching the accuracy of marker-based methods, supporting its potential for use in clinical settings. MMC could provide valuable insights into movement rehabilitation in stroke patients, potentially enhancing the effectiveness of rehabilitation strategies.

翻译：基于标记的光学运动捕捉（OMC）结合生物力学建模目前被认为是测量人体运动学最精确的方法。然而，将可微分生物力学建模与无标记运动捕捉（MMC）相结合，为临床环境中的运动捕捉提供了一种前景广阔的方法，该方法仅需最简设备（如同步网络摄像头）且数据采集工作量极小。本研究比较了15名卒中患者执行饮水任务（一种推荐用于评估上肢运动质量的功能性任务）时，基于生物力学建模的MMC与OMC数据的关键运动学结果。我们观察到MMC与OMC在运动学轨迹上具有高度一致性，具体表现为高相关性（多数运动学轨迹的中位数r值高于0.95）以及中位数RMSE值范围：关节角度为2-5度，末端效应器速度为0.04 m/s，躯干位移为6 mm。OMC与MMC在单次试验间的偏差在参与者测试会话内保持稳定，其偏差的四分位距范围约为：关节角度1-3度，末端效应器速度0.01 m/s，躯干位移约3mm。我们的研究结果表明，用于手臂追踪的MMC正接近基于标记方法的精度，支持其在临床环境中应用的潜力。MMC可为卒中患者的运动康复提供有价值的见解，并可能提升康复策略的有效性。