Most conventional studies on tennis serve biomechanics rely on phenomenological observations comparing professional and amateur players or, more recently, on AI-driven statistical analyses of motion data. While effective at describing \textit{what} elite players do, these approaches often fail to explain \textit{why} such motions are physically necessary from a mechanistic perspective. This paper proposes a deterministic, physics-based approach to the tennis serve using a 12-degree-of-freedom multi-segment model of the human upper body. Rather than fitting the model to motion capture data, we solve the inverse kinematics problem via trajectory optimization to rigorously satisfy the aerodynamic boundary conditions required for Flat, Slice, and Kick serves. We subsequently perform an inverse dynamics analysis based on the Principle of Virtual Power to compute the net joint torques. The simulation results reveal that while the kinematic trajectories for different serves may share visual similarities, the underlying kinetic profiles differ drastically. A critical finding is that joints exhibiting minimal angular displacement (kinematically ``quiet'' phases), particularly at the wrist, require substantial and highly time-varying torques to counteract gravitational loading and dynamic coupling effects. By elucidating the dissociation between visible kinematics and internal kinetics, this study provides a first-principles framework for understanding the mechanics of the tennis serve, moving beyond simple imitation of elite techniques.
翻译:传统网球发球生物力学研究大多依赖于职业与业余选手对比的现象学观察,或近期基于运动数据的AI统计分析。这些方法虽能有效描述精英选手的"动作表现",却往往无法从力学机制角度解释此类运动为何具有物理必要性。本文提出一种基于物理的确定性方法,通过包含12个自由度的上肢多刚体模型研究网球发球。区别于传统的运动捕捉数据拟合,我们采用轨迹优化方法求解逆运动学问题,严格满足平击、侧旋和上旋发球所需的空气动力学边界条件。随后基于虚功率原理进行逆动力学分析,计算净关节扭矩。仿真结果表明:不同发球方式的运动学轨迹可能视觉相似,但底层动力学特征差异显著。关键发现是,在角位移极小(运动学"静止"阶段)的关节处——特别是腕关节——需要施加显著且高度时变的扭矩以抵消重力载荷与动态耦合效应。通过阐明可见运动学与内部动力学之间的解耦关系,本研究建立了理解网球发球力学的第一性原理框架,超越了单纯对精英技术的模仿。