Hobbies

Cutting-Edge Golf Swing Analysis via MediaPipe and Machine Learning

April 23, 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 have explored using MediaPipe, a Google open-source framework, combined with machine learning to perform 2D human pose estimation on mobile devices for golf swing analysis. Their studies focus on joint angle measurements and automated pose grading to evaluate swing quality without expensive hardware.

Why it matters

This research underscores a democratization of sports analytics by enabling sophisticated biomechanical feedback through accessible mobile technology. Users learn that accurate performance assessment no longer requires specialized equipment but can be achieved with machine learning models analyzing video data in real time.

Who's doing it

Academic groups publishing in the IEEE International Conference on Image Processing and Springer Nature are pioneering these methods, demonstrating feasibility and reliability of mobile-based pose estimation for golf swing improvement.

Try it

1. Download a MediaPipe pose estimation demo app or build your own using the MediaPipe framework (https://mediapipe.dev).
2. Record golf swings on your smartphone and process the video through the pose estimation model to extract key joint angles.
3. Apply machine learning algorithms, such as those from TensorFlow Lite, to grade swing quality and receive actionable feedback for improvement.

Read the original at link.springer.com