# Learning golf swing signatures from a single wrist-worn inertial sensor

arXiv: https://arxiv.org/abs/2506.17505 · HTML: https://mlsanity.com/paper/2506.17505/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 36 references that are in the map.
- First posted on arXiv 20 Jun 2025.

## Builds on (most cited first, 15 of 15)

- [Neural Discrete Representation Learning](https://mlsanity.com/paper/1711.00937/) (2017)
- [Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation](https://mlsanity.com/paper/1308.3432/) (2013)
- [Generating Diverse High-Fidelity Images with VQ-VAE-2](https://mlsanity.com/paper/1906.00446/) (2019)
- [AMASS: Archive of Motion Capture as Surface Shapes](https://mlsanity.com/paper/1904.03278/) (2019)
- [On the Continuity of Rotation Representations in Neural Networks](https://mlsanity.com/paper/1812.07035/) (2018)
- [Action-Conditioned 3D Human Motion Synthesis with Transformer VAE](https://mlsanity.com/paper/2104.05670/) (2021)
- [Finite Scalar Quantization: VQ-VAE Made Simple](https://mlsanity.com/paper/2309.15505/) (2023)
- [WHAM: Reconstructing World-grounded Humans with Accurate 3D Motion](https://mlsanity.com/paper/2312.07531/) (2023)
- [Generating Holistic 3D Human Motion from Speech](https://mlsanity.com/paper/2212.04420/) (2022)
- [Avatars Grow Legs: Generating Smooth Human Motion from Sparse Tracking Inputs with Diffusion Model](https://mlsanity.com/paper/2304.08577/) (2023)
- [Software for Dataset-wide XAI: From Local Explanations to Global Insights with Zennit, CoRelAy, and ViRelAy](https://mlsanity.com/paper/2106.13200/) (2021)
- [ParCo: Part-Coordinating Text-to-Motion Synthesis](https://mlsanity.com/paper/2403.18512/) (2024)
- [MoGenTS: Motion Generation based on Spatial-Temporal Joint Modeling](https://mlsanity.com/paper/2409.17686/) (2024)
- [GolfDB: A Video Database for Golf Swing Sequencing](https://mlsanity.com/paper/1903.06528/) (2019)
- [Explaining the Unique Nature of Individual Gait Patterns with Deep Learning](https://mlsanity.com/paper/1808.04308/) (2018)

## Built on by (most cited first, 1 of 1)

- [Full-Body Golf Swing Kinematic Reconstruction From a Smartwatch IMU](https://mlsanity.com/paper/2606.22876/) (2026)

## Abstract (arXiv)

Despite its importance for performance and injury prevention, golf swing analysis is limited by isolated metrics, underrepresentation of professional athletes, and a lack of rich, interpretable movement representations. We address these gaps with a holistic, data-driven framework for personalized golf swing analysis from a single wrist-worn sensor. We build a large dataset of professional swings from publicly available videos, reconstruct full-body 3D kinematics using biologically accurate human mesh recovery, and generate synthetic inertial data to train neural networks that infer motion and segment swing phases from wrist-based input. We learn a compositional, discrete vocabulary of motion primitives that facilitates the detection and visualization of technical flaws, and is expressive enough to predict player identity, club type, sex, and age. Our system accurately estimates full-body kinematics and swing events from wrist data, delivering lab-grade motion analysis on-course and supporting early detection of anomalous movement patterns. Explainability methods reveal subtle, individualized movement signatures, reinforcing the view that variability is a hallmark of skilled performance. Longitudinal tracking demonstrates practical value: as one player's handicap improved from 50 to 2.2 over 1.5 years, our system captured measurable technical progress and provided targeted, actionable feedback. Our findings challenge common assumptions, such as swing consistency across clubs and the existence of a single "ideal" swing, and uncover latent biomarkers shaped by both intrinsic traits and task-specific constraints. This work bridges lab and field-based biomechanics, offering scalable, accessible, high-fidelity motion analysis for research, coaching, and injury prevention, while opening new directions in movement-based phenotyping, personalized equipment design, and motor skill development.