Full-Body Golf Swing Kinematic Reconstruction From a Smartwatch IMU
2026-06-22 • Computer Vision and Pattern Recognition
Computer Vision and Pattern Recognition
AI summaryⓘ
The authors developed a method to measure how the whole body moves during a golf swing using just one sensor worn on the wrist. Their system, called WIT-KinNet, uses data from this wrist sensor and smart computer models to estimate the angles of all body joints while swinging. They tested it on 36 golfers using different clubs and verified the results against a precise camera system, finding their estimates were quite accurate. Factors like the type of swing, player skill, and club used affected the accuracy. This work shows it's possible to analyze golf swings practically on the course with just one wrist-worn sensor.
Inertial Measurement Unit (IMU)Golf Swing KinematicsJoint Angle EstimationWrist-Worn SensorTemporal Kinematic EncodingCross-ValidationX-factorS-factorMotion CaptureMachine Learning
Authors
Yuanshuo Tan, Kezhe Zhu, Xiujie Sun, Chunping Liang, Shuoyang Zhu, Chenquan Xu, Licheng Zhong, Huiming Pan, Yinri Jin, Chang Liu, Bo Xiao, Shenglong Le, Bryndan W. Lindsey, Peter B. Shull
Abstract
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) leverages modality-specific IMU embeddings and temporal kinematic encoding to learn wrist-to-body motion dependencies and estimate full-body joint angles during golf swings. Thirty-six golfers spanning 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 system. The proposed approach achieved a mean absolute error of 8.11 $\pm$ 1.84$^\circ$ across full-body joint angles. High temporal correlation was observed for pelvic rotation and upper torso rotation (r = 0.98 and 0.97, respectively), with X-factor and S-factor also showing strong correlation (r = 0.96 and 0.96). Linear mixed-effects models of the error revealed that swing amplitude, skill level, and club type all significantly affected measurement differences (p $<$ 0.05). The results establish the first single wrist-worn IMU approach for estimating full-body golf swing kinematics, enabling practical swing analysis during real gameplay.