Martinez Caitlyn, Garbett Seth, Hiromasa Kristen, Jackson Rhandi, Miya Eric, Miya Michelle, White Joshua D, Baum Brian S, Reinking Mark F
School of Physical Therapy Regis University.
Lincoln Laboratory Massachusetts Institute of Technology.
Int J Sports Phys Ther. 2022 Jun 1;17(4):566-573. doi: 10.26603/001c.34432. eCollection 2022.
Providing clinicians with an accurate method to predict kinetic measurements using 2D kinematic motion analysis is crucial to the management of distance runners. Evidence is needed to compare the accuracy of 2D and 3D kinematic measurements as well as measured and estimated kinetic variables.
The objectives of this study were to (1) compare 2D video analysis of running kinematics with gold standard 3D motion capture and, (2) to evaluate published equations which estimate running kinetics using 2D kinematic and spatiotemporal values and modify these equations based on study findings.
Controlled laboratory study, cross-sectional design.
Runners who averaged at least 20 miles per week were invited to participate. Athletes ran on an instrumented treadmill at their preferred training pace for a 6-minute warm-up. Markers were placed over designated anatomical landmarks on both sides of the pelvis as well as the left lower extremity. Subjects then ran at their preferred speed and kinematic data were recorded using both the 2D and 3D camera systems at 240 frames/second. Additionally, ground reaction forces were recorded at 1200Hz. 2D and 3D kinematic values were compared and published kinetic prediction formulas were tested. Linear regression was used to develop new prediction equations for average loading rate (AVG_LR), peak vertical ground reaction force (VERT_GRF), and peak braking force (PK_BRK). Paired t-tests were used to assess differences between the 2D and 3D kinematic variables and the measured (MEAS) and calculated (CALC) kinetic variables.
Thirty runners (13 men and 17 women) voluntarily consented to participate in this study and the mean age of the participants was 31.8 years (range 20 to 48 years). Although significant differences existed, all 2D kinematic measures were within 2°-5° of 3D kinematic measures. Published prediction equations for AVG_LR and VERT_GRF were supported, but new prediction equations showed higher R for AVG_LR (0.52) and VERT_GRF (0.75) compared to previous work. A new prediction equation for PK_BRK was developed. No significant differences were found between the MEAS and CALC kinetic variables using the new equations.
Accurate predictions of kinetic variables can be made using spatiotemporal and 2D kinematic variables.
Level 2.
为临床医生提供一种使用二维运动学分析来预测动力学测量值的准确方法，对于长跑运动员的管理至关重要。需要证据来比较二维和三维运动学测量的准确性，以及测量和估计的动力学变量。
本研究的目的是：（1）将跑步运动学的二维视频分析与金标准三维运动捕捉进行比较；（2）评估使用二维运动学和时空值估计跑步动力学的已发表方程，并根据研究结果对这些方程进行修改。
对照实验室研究，横断面设计。
邀请平均每周至少跑20英里的跑步者参与。运动员在仪器化跑步机上以其偏好的训练速度进行6分钟的热身。在骨盆两侧以及左下肢的指定解剖标志上放置标记。然后受试者以其偏好的速度跑步，并使用二维和三维摄像系统以240帧/秒的速度记录运动学数据。此外，以1200Hz记录地面反作用力。比较二维和三维运动学值，并测试已发表的动力学预测公式。使用线性回归为平均负荷率（AVG_LR）、峰值垂直地面反作用力（VERT_GRF）和峰值制动力（PK_BRK）建立新的预测方程。使用配对t检验评估二维和三维运动学变量以及测量（MEAS）和计算（CALC）动力学变量之间的差异。
30名跑步者（13名男性和17名女性）自愿同意参与本研究，参与者的平均年龄为31.8岁（范围20至48岁）。尽管存在显著差异，但所有二维运动学测量值均在三维运动学测量值的2°-5°范围内。已发表的AVG_LR和VERT_GRF预测方程得到支持，但新的预测方程显示AVG_LR（0.52）和VERT_GRF（0.75）的R值比以前的工作更高。开发了PK_BRK的新预测方程。使用新方程时，测量和计算的动力学变量之间未发现显著差异。
使用时空和二维运动学变量可以对动力学变量进行准确预测。
2级。
