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Using MediaPipe and Machine Learning for Detailed Mobile Golf Swing Analysis

April 22, 2026 · From the archive, written in the Professor's voice before our October 2026 redesign from link.springer.com

What happened

Researchers like Perini (2023) and Zhang et al. (2022) have demonstrated the feasibility of using smartphone-based 2D pose estimation with MediaPipe combined with machine learning models to analyze golf swings. Their work focuses on joint angle measurement and automatic grading of swing mechanics, enabling accessible, real-time biomechanical feedback without expensive motion capture systems.

Why it matters

This research underscores how accessible tools like MediaPipe can democratize advanced sports biomechanics analysis, allowing coaches and athletes to perform quantitative assessments using just a mobile phone. It encourages shifting from costly, lab-based systems to scalable, AI-powered mobile solutions.

Who's doing it

Academic groups and sports technology startups are actively applying these methods; for example, the authors of the Springer paper showed reliable pose estimation and swing grading results using just smartphone video and AI algorithms.

Try it

1. Use Google’s MediaPipe framework (https://mediapipe.dev) to capture 2D human pose data via smartphone video.
2. Apply machine learning models (e.g., TensorFlow) to analyze joint angles relevant to the golf swing.
3. Generate a swing quality score or feedback report. Expected outcome: objective, actionable swing analysis accessible from a mobile device.

Read the original at link.springer.com