Full-Body Golf Swing Kinematic Reconstruction From a Smartwatch IMU

2026-06-22

AI Summary

This study addresses the challenge of quantifying golf swing kinematics in real-world settings, where existing methods rely on multi-sensor systems or fixed equipment that hinder practical deployment on actual courses. To overcome this limitation, we propose the Wrist-IMU Temporal Kinematic Network (WIT-KinNet), which leverages a single inertial measurement unit (IMU) embedded in a wrist-worn smartwatch to model the dynamic relationship between wrist motion and full-body joint kinematics through temporal kinematic encoding. Evaluated across 36 golfers of varying skill levels, multiple swing types, and club conditions, our approach achieves a mean joint angle error of 8.11° ± 1.84°, with pelvis and torso rotation correlations of 0.98 and 0.97, respectively, and X-factor and S-factor correlations both at 0.96. This work significantly reduces hardware complexity while enabling accurate, real-scenario swing analysis.

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.

Problem

• golf swing
• full-body kinematics
• inertial measurement unit
• smartwatch
• motion capture

Innovation

• single-wrist IMU
• full-body kinematics
• golf swing analysis
• temporal kinematic encoding
• WIT-KinNet