# Full-Body Golf Swing Kinematic Reconstruction From a Smartwatch IMU

arXiv: https://arxiv.org/abs/2606.22876 · HTML: https://mlsanity.com/paper/2606.22876/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 57 references that are in the map.
- First posted on arXiv 22 Jun 2026.

## Builds on (most cited first, 10 of 10)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [FiLM: Visual Reasoning with a General Conditioning Layer](https://mlsanity.com/paper/1709.07871/) (2017)
- [Conformer: Convolution-augmented Transformer for Speech Recognition](https://mlsanity.com/paper/2005.08100/) (2020)
- [An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling](https://mlsanity.com/paper/1803.01271/) (2018)
- [On the Continuity of Rotation Representations in Neural Networks](https://mlsanity.com/paper/1812.07035/) (2018)
- [WHAM: Reconstructing World-grounded Humans with Accurate 3D Motion](https://mlsanity.com/paper/2312.07531/) (2023)
- [MotionBERT: A Unified Perspective on Learning Human Motion Representations](https://mlsanity.com/paper/2210.06551/) (2022)
- [SmoothNet: A Plug-and-Play Network for Refining Human Poses in Videos](https://mlsanity.com/paper/2112.13715/) (2021)
- [GolfDB: A Video Database for Golf Swing Sequencing](https://mlsanity.com/paper/1903.06528/) (2019)
- [Learning golf swing signatures from a single wrist-worn inertial sensor](https://mlsanity.com/paper/2506.17505/) (2025)

## Abstract (arXiv)

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±2.05^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.