Spinal Movement Biomechanics Group, Division of Physiotherapy, School of Health Professions, Bern University of Applied Sciences, Bern, Switzerland; Department of Sport, Exercise and Health, University of Basel, Basel, Switzerland.
Department of Sport, Exercise and Health, University of Basel, Basel, Switzerland.
J Biomech. 2024 Feb;164:111975. doi: 10.1016/j.jbiomech.2024.111975. Epub 2024 Feb 2.
Whole-body lifting strategies could be derived from conventional video recordings using the Stoop-Squat-Index, which quantifies the ratio between trunk forward lean and lower extremity joint flexion from 0 (full squat) to 100 (full stoop). The purpose of this study was to compare Stoop-Squat-Indices derived from conventional video recordings to those from a three-dimensional marker-based motion capture system and to evaluate interrater and intrarater reliability of the video-based approach. Thirty healthy participants lifted a 5-kg box under different conditions (freestyle, squat, stoop). Kinematic data were recorded using a Vicon motion capture system (serving as reference standard) and an iPad camera. Stoop-Squat-Indices over the entire lifting cycle were derived separately from both approaches. Agreement was assessed using mean differences (video minus motion capture) and limits of agreement. Reliability was investigated by calculating intraclass correlation coefficients (ICC) and minimal detectable changes (MDC) over the course of the lifting cycle. Systematic errors were identified with Statistical Parametric Mapping-based T-tests. Systematic errors between the video-based and the motion capture-based approach were observed among all conditions. Mean differences in Stoop-Squat-Indices over the lifting cycle ranged from -6.9 to 3.2 (freestyle), from -1.8 to 5.3 (squat) and from -2.8 to -1.1 (stoop). Limits of agreement were lower when the box was close to the floor, and higher towards upright standing. Reliability of the video-based approach was excellent for most of the lifting cycle, with ICC above 0.995 and MDC below 3.5. These findings support using a video-based assessment of Stoop-Squat-Indices to quantify whole-body lifting strategy in field.
全身提升策略可以从常规视频记录中得出,使用 Stoop-Squat-Index 来量化躯干前倾与下肢关节弯曲的比值,范围从 0(完全深蹲)到 100(完全弯腰)。本研究的目的是比较常规视频记录和基于三维标记的运动捕捉系统得出的 Stoop-Squat-Indices,并评估基于视频方法的组内和组间可靠性。30 名健康参与者在不同条件下(自由式、深蹲、弯腰)提起一个 5 公斤的盒子。运动捕捉系统(作为参考标准)和 iPad 摄像头记录运动学数据。分别从两种方法得出整个提升周期的 Stoop-Squat-Indices。使用视频与运动捕捉的平均差值(video minus motion capture)和一致性界限来评估一致性。通过计算整个提升周期的组内相关系数(ICC)和最小可检测变化(MDC)来研究可靠性。使用基于统计参数映射的 T 检验来识别系统误差。在所有条件下,基于视频和基于运动捕捉的方法之间都观察到了系统误差。在整个提升周期中,Stoop-Squat-Indices 的平均差异在自由式时为-6.9 到 3.2,在深蹲时为-1.8 到 5.3,在弯腰时为-2.8 到-1.1。当盒子靠近地面时,一致性界限较低,当处于直立站立时,一致性界限较高。基于视频的方法在大多数提升周期的可靠性都很高,ICC 高于 0.995,MDC 低于 3.5。这些发现支持在现场使用基于视频的 Stoop-Squat-Indices 评估来量化全身提升策略。