PURPOSE This study aimed to provide evidence for the interpretation of the projected frontal area (PFA) during front crawl. To achieve this goal, we developed a method for calculating the PFA of each body segment using digital human technology and compared the pressure drag under two calculation conditions: a combination of the PFA with and without accounting for the horizontal velocity of each body segment. METHODS Twelve competitive male swimmers performed a 15-meter front crawl at 1.20 m·s-1. The three-dimensional positions of the reflective markers attached to the swimmer's body were recorded using an underwater motion-capture system. Based on the body shape of each swimmer obtained from the photogenic body scanner, individual digital human body models were created with the color of the model's vertices divided into eight body segments. The time series of the volumetric swimming motion was reconstructed using inverse kinematics. The PFA of each body segment was then calculated by the automatic processing of a series of parallel frontal images. The pressure drag index, defined as the value excluding the drag coefficient while simultaneously considering the PFA and the horizontal velocity, was calculated under two conditions: the static condition (accounting for only the PFA of each body segment) and the dynamic condition (accounting for the PFA and horizontal velocity of each body segment). RESULTS Notably, the pressure drag index was higher under the static condition than under the dynamic condition for the humerus, ulna, and hand segments (p < 0.001). CONCLUSIONS The results obtained using our methodology indicate that the PFA of the upper limb segments overestimates their contribution to pressure drag during front crawl under the static condition.

中文翻译：

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解释 Front Crawl 中的投影额叶面积：确定每个身体部分的投影额叶面积。


目的 本研究旨在为前爬行过程中投影额面积 （PFA） 的解释提供证据。为了实现这一目标，我们开发了一种使用数字人体技术计算每个体段 PFA 的方法，并比较了两种计算条件下的压力阻力：PFA 的组合，考虑了和不考虑每个体段的水平速度。方法 12 名竞技男游泳运动员以 1.20 m·s-1 的速度进行了 15 m 的前爬。使用水下动作捕捉系统记录附着在游泳者身上的反光标记的三维位置。根据从上镜人体扫描仪获得的每位游泳者的体型，创建了单独的数字人体模型，并将模型顶点的颜色分为八个身体部分。使用逆运动学重建了体积游泳运动的时间序列。然后通过自动处理一系列平行额叶图像来计算每个体段的 PFA。压力阻力指数定义为不包括阻力系数的值，同时考虑 PFA 和水平速度，在两个条件下计算：静态条件（仅考虑每个体段的 PFA）和动态条件（考虑每个体段的 PFA 和水平速度）。结果 值得注意的是，肱骨、尺骨和手节在静态条件下的压力阻力指数高于动态条件下 （p < 0.001）。结论使用我们的方法获得的结果表明，在静态条件下，上肢节段的 PFA 高估了它们在前爬行过程中对压力阻力的贡献。