Lyu Yuanyuan, Duan Xiaoling, Yang Chen, Ye Qiang
School of Sport and Health Science, Nanjing Sport Institute, Nanjing, China.
School of Table Tennis and Badminton, Nanjing Sport Institute, Nanjing, China.
Front Sports Act Living. 2025 Aug 15;7:1635581. doi: 10.3389/fspor.2025.1635581. eCollection 2025.
This study aimed to quantify kinematic relationships across body segments during forehand strokes to provide interpretable metrics for single-camera based lightweight table tennis diagnostics.
We analyzed 34 female players (aged 9.1-21.7 years) from provincial teams, recording a total of 340 strokes (10 per player). An SVM model was used to predict ball speed, after which 320 strokes (8-10 per player) were retained by removing outliers in ball speed. From MediaPipe position time series, we calculated velocity, angle and angular velocity time series, and extracted kinematic parameters from these time series, including range, mean/peak/impact values. Within-subject correlation coefficients ( ) were calculated to identify key biomechanical parameters that contribute to the ball speed, while between-subject correlation coefficients ( ) were used to detect the relationship between age/height and ball speed.
Ball speed increased with greater playing-side arm linear movement at the shoulder ( = 0.51 to 0.63), elbow ( = 0.63 to 0.70) and wrist ( = 0.50 to 0.60), as well as with enhanced rotational motion at the playing-side upper arm ( = 0.65 to 0.71), shoulder line ( = 0.54 to 0.57), and hip line ( = 0.51 to 0.59). Conversely, ball speed decreased with excessive contralateral shoulder horizontal flexion/extension ( = -0.44 to -0.62) and playing-side elbow flexion-extension ( = -0.35). At the population-level, ball speed increases with age before 14.3 years ( = 0.68) but plateaus thereafter ( = 0.17).
This MediaPipe-based framework demonstrates potential for efficient biomechanical analysis in table tennis, providing a promising foundation for lightweight real-time analysis solutions.
本研究旨在量化正手击球过程中身体各部位之间的运动学关系，以便为基于单摄像头的轻量化乒乓球诊断提供可解释的指标。
我们分析了来自省级球队的34名女性球员（年龄在9.1 - 21.7岁之间），共记录了340次击球（每人10次）。使用支持向量机（SVM）模型预测球速，之后通过去除球速异常值保留了320次击球（每人8 - 10次）。从MediaPipe位置时间序列中，我们计算了速度、角度和角速度时间序列，并从这些时间序列中提取运动学参数，包括范围、均值/峰值/冲击值。计算受试者内相关系数（ ）以确定影响球速的关键生物力学参数，同时使用受试者间相关系数（ ）来检测年龄/身高与球速之间的关系。
球速随着击球侧手臂在肩部（ = 0.51至0.63）、肘部（ = 0.63至0.70）和腕部（ = 0.50至0.60）的更大直线运动，以及击球侧上臂（ = 0.65至0.71）、肩部连线（ = 0.54至0.57）和髋部连线（ = 0.51至0.59）的旋转运动增强而增加。相反，球速随着对侧肩部过度的水平屈伸（ = -0.44至 -0.62）和击球侧肘部屈伸（ = -0.35）而降低。在总体水平上，球速在14.3岁之前随年龄增加（ = 0.68），但此后趋于平稳（ = 0.17）。
这个基于MediaPipe的框架展示了在乒乓球运动中进行高效生物力学分析的潜力，为轻量化实时分析解决方案提供了一个有前景的基础。
