Purpose The objective of this study was to determine the agreement of kinematic parameters calculated from motion data collected via a 2D video-based pose-estimation (markerless motion capture) approach and a laboratory-based 3D motion capture approach during a floor-to-waist height functional lifting test. Method Twenty healthy participants each performed three floor-to-waist height lifts. Participants’ lifts were captured simultaneously using 2D video (camcorder) in the sagittal plane and 3D motion capture (Vicon, Oxford, UK). The three lifts were representative of a perceived light, medium, and heavy load. Post-collection, video data were processed through a pose-estimation software (i.e., markerless motion capture). Motion data from 3D motion capture and video-based markerless motion capture were each used to calculate objective measures of interest relevant to a functional capacity evaluation (i.e., posture, balance, distance of the load from the body, and coordination). Bland–Altman analyses were used to calculate agreement between the two methods. Results Bland–Altman analysis revealed that mean differences ranged from 1.9° to 22.1° for posture and coordination-based metrics calculated using markerless and 3D motion capture, respectively. Limits of agreement for most posture and coordination measures were approximately + 20°. Conclusions 2D video-based pose estimation offers a strategy to objectively measure movement and subsequently calculated metrics of interest within an FCE context and setting, but at present the agreement between metrics calculated using 2D video-based methods and 3D motion capture is insufficient. Therefore, continued effort is required to improve the accuracy of 2D-video based pose estimation prior to inclusion into functional testing paradigms.

目的本研究的目的是确定从通过基于 2D 视频的姿势估计（无标记运动捕捉）方法和基于实验室的 3D 运动捕捉方法收集的运动数据计算的运动学参数的一致性。高度功能提升测试。方法20 名健康参与者每人进行了 3 次从地板到腰部的高度提升。在矢状面使用 2D 视频（摄像机）和 3D 动作捕捉（Vicon，牛津，英国）同时捕捉参与者的举重动作。三部升降机分别代表感知的轻、中、重负载。收集后，通过姿势估计软件（即无标记运动捕捉）处理视频数据。来自 3D 运动捕捉和基于视频的无标记运动捕捉的运动数据分别用于计算与功能能力评估相关的客观测量指标（即姿势、平衡、负载与身体的距离和协调性）。Bland-Altman 分析用于计算两种方法之间的一致性。结果Bland-Altman 分析显示，分别使用无标记和 3D 运动捕捉计算的基于姿势和协调的指标的平均差异范围为 1.9° 到 22.1°。大多数姿势和协调措施的一致性限制约为 + 20°。结论基于 2D 视频的姿态估计提供了一种在 FCE 上下文和设置中客观测量运动和随后计算的感兴趣指标的策略，但目前使用基于 2D 视频的方法计算的指标与 3D 运动捕捉之间的一致性还不够。因此，在纳入功能测试范例之前，需要继续努力提高基于 2D 视频的姿态估计的准确性。