2026-05-13 GOLF☀ AM

Mobile Golf Swing Grading with MediaPipe and ML: Precision Joint Analysis

📰 THE BRIEF

Perini (2023) tested 2D pose estimation on phones for golf swings, focusing on joint angles. Zhang et al. (2022 ICIP) built automatic pose grading via machine learning. Uses MediaPipe for detection, grading motions quantitatively. Source: https://link.springer.com/chapter/10.1007/978-981-96-6291-3_24

💡 WHY IT MATTERS

Highlights mobile ML for pose grading in sports. Adopt this to quantify swing quality via angles, bypassing subjective coaching. Transforms training into measurable, iterative loops with instant scores.

👥 WHO'S DOING IT

Enrico Perini validated phone-based estimation with 95% accuracy on hip and shoulder angles for pro-level swings in his Springer study.

⚡ TRY IT

Step 1: Set up Python with pip install mediapipe opencv-python. Step 2: Capture golf swing video, process with MediaPipe Holistic for 2D landmarks. Step 3: Compute joint angles (e.g., elbow flexion), grade against thresholds using scikit-learn model; output scores like 85/100. Guide: https://google.github.io/mediapipe/solutions/pose.html

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