Estimation of Upper Limb Kinematics in Baseball Pitching Using Sensor-embedded Ball

We estimated the kinematics of the upper limb during baseball pitching using a sensor-embedded ball. Twenty collegiate pitchers performed 24 trials each, throwing fastballs, curveballs, and sliders. Kinematic data were collected using motion capture system and a sensor-embedded baseball. Machine learning models, including Linear Regression, Lasso, Random Forest, Gradient Boosting, and Support Vector Regression (SVR), were used to estimate joint angles. The evaluation metric was the R² score from 10-fold cross-validation. Random Forest (R² score (the average of all joint movement) = 0.85) and Gradient Boosting showed high accuracy (R² score (the average of all joint movement) = 0.85), particularly for shoulder and elbow joints. The ensemble model further improved accuracy. The model demonstrated high accuracy in estimating joint angles for the shoulder joint (external/internal rotation) (R² score (the average of all time) = 0.97) and elbow joint (supination/pronation) (R² score (the average of all time) = 0.96). In the future, the application of this model is expected to facilitate the acquisition of kinematic data of the pitching arm in competitive environments.