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Leveraging MediaPipe for Precise Golf Swing Joint Angle Analysis

April 21, 2026 · From the archive, written in the Professor's voice before our October 2026 redesign from link.springer.com

What happened

Researchers including Perini and Zhang utilized MediaPipe's 2D human pose estimation combined with machine learning algorithms to analyze golf swings via mobile phone video. The study focused on quantifying selected joint angles to provide real-time feedback on swing mechanics, demonstrating feasibility for mobile-based sports analytics with high accuracy. This work was documented in Springer Nature's 2023 publication and the 2022 IEEE ICIP conference proceedings.

Why it matters

This case exemplifies how integrating open-source pose estimation frameworks like MediaPipe with machine learning enables accessible, detailed biomechanical analysis without expensive hardware. It shifts the workflow toward mobile-first, data-driven coaching, allowing athletes and coaches to quantify motion objectively and adjust technique based on precise joint metrics rather than subjective observation.

Who's doing it

Academic groups led by Perini and Zhang at institutions publishing with Springer Nature and IEEE are pioneering this approach, showcasing scalable, low-cost solutions for sports performance analysis with validated results in joint angle accuracy and movement grading.

Try it

1. Record a golf swing using a standard smartphone camera.
2. Use MediaPipe's Pose module (https://google.github.io/mediapipe/solutions/pose) to extract 2D joint landmarks from the video frames.
3. Apply a machine learning model, such as a custom regression or classification algorithm, to evaluate joint angles and grade the swing. Outcome: Obtain quantitative feedback on swing mechanics that can be used to guide training adjustments.

Read the original at link.springer.com