Full-Body Golf Swing Kinematic Reconstruction From a Smartwatch IMU
Quantitative measurement of the golf swing is critical for evaluating technique and enabling individualized feedback. However, existing methods are impractical to use on the golf course: optical motion capture is laboratory-bound, camera-based methods require impractical camera placement, and multi-sensor inertial measurement unit (IMU) systems require multi-segment setup and calibration. We thus propose a single wrist-worn IMU approach for estimating full-body joint angles during golf swings. The proposed Wrist-IMU Temporal Kinematic Network (WIT-KinNet) combines IMU embedding, feature-wise linear modulation (FiLM), and temporal convolutional encoding. Thirty-six golfers, comprising beginner and skilled players, performed full, half, and quarter swings using seven club types: driver, 3-wood, 5-hybrid, 5-iron, 7-iron, 9-iron, and sand wedge. The proposed WIT-KinNet was evaluated under subject-wise cross-validation using synchronized smartwatch IMU data and ground-truth kinematics derived from an optical motion capture (OMC) system, with OMC-free IMU calibration and IMU-based swing segmentation. The proposed approach achieved a mean absolute error of $8.53\pm2.05^\circ$ across full-body joint angles. High temporal correlation was observed for pelvic rotation and upper torso rotation ($r=0.99$ for both), with X-factor and S-factor also showing strong correlations ($r=0.97$ for both). Linear mixed-effects models of the error revealed that swing amplitude significantly affected estimation error across all five trunk--pelvis variables ($p<0.05$). The results demonstrate the feasibility of full-body golf swing kinematic estimation from a commercial smartwatch, providing a potential solution for post-swing biomechanical analysis.