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Prediction of Kinematic and Kinetic Performance in a Drop Vertical Jump with Individual Anthropometric Factors in Adolescent Female Athletes: Implications for Cadaveric Investigations

机译:预测的下降和垂直运动下降与个人人体测量学因素的运动的动力学性能的青少年女运动员:对尸体调查的意义

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摘要

Anterior cruciate ligament injuries are common, expensive to repair, and often debilitate athletic careers. Robotic manipulators have evaluated knee ligament biomechanics in cadaveric specimens, but face limitations such as accounting for variation in bony geometry between specimens that may influence dynamic motion pathways. This study examined individual anthropometric measures for significant linear relationships with in vivo kinematic and kinetic performance and determined their implications for robotic studies. Anthropometrics and 3D motion during a 31 cm drop vertical jump task were collected in high school female basketball players. Anthropometric measures demonstrated differential statistical significance in linear regression models relative to kinematic variables (P-range < 0.01-0.95). However, none of the anthropometric relationships accounted for clinical variance or provided substantive univariate accuracy needed for clinical prediction algorithms (r2 < 0.20). Mass and BMI demonstrated models that were significant (P < 0.05) and predictive (r2 > 0.20) relative to peak flexion moment, peak adduction moment, flexion moment range, abduction moment range, and internal rotation moment range. The current findings indicate that anthropometric measures are less associated with kinematics than with kinetics. Relative to the robotic manipulation of cadaveric limbs, the results do not support the need to normalize kinematic rotations relative to specimen dimensions.
机译:前十字韧带损伤很常见,修复费用昂贵,并且常常使体育事业衰弱。机器人操纵器已经评估了尸体标本中的膝盖韧带生物力学,但是面临着局限性,例如考虑了标本之间的骨骼几何形状的变化,这可能会影响动态运动路径。这项研究检查了各个人体测量学指标与体内运动学和动力学性能之间的显着线性关系,并确定了它们对机器人研究的影响。在高中女生篮球运动员中收集了31厘米下降垂直跳高任务期间的人体测量学和3D运动。人体测量学表明,线性回归模型相对于运动学变量具有统计学差异(P范围<0.01-0.95)。但是,没有任何人体测量学关系能够解释临床差异或提供临床预测算法所需的实质单变量准确性(r 2 <0.20)。质量和BMI证明相对于最大屈曲力矩,峰值内收力矩,屈曲力矩范围,外展力矩范围和内部旋转力矩,模型具有显着性(P <0.05)和预测性(r 2

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