Cutting-Edge Golf Swing Analysis Leveraging MediaPipe and Machine Learning Breaks New Ground
Researchers including Perini and Zhang have demonstrated the feasibility of using mobile phone-based 2D human pose estimation frameworks, specifically MediaPipe, combined with machine learning classifiers to analyze golf swings. Their studies, published via Springer Nature and IEEE ICIP 2022, focus on automatically grading swing quality by examining selected joint angles, providing a low-cost, accessible alternative to traditional motion capture systems.
This research illustrates a practical application of pose estimation technology outside laboratory settings, enabling golfers and coaches to obtain detailed biomechanical feedback using only a smartphone. It shifts thinking toward democratizing sports analytics through AI, making sophisticated performance analysis more widely available and scalable without expensive equipment.
Academic groups led by Perini and Zhang have validated these techniques, showing that mobile 2D pose estimation combined with machine learning can reliably assess golf swing mechanics and potentially improve training outcomes.
Step 1: Install the MediaPipe Pose framework from https://mediapipe.dev and set up the environment to capture golf swing video on a smartphone. Step 2: Extract 2D joint coordinates focusing on critical angles such as elbow and shoulder during the swing phases. Step 3: Train a machine learning model (e.g., an SVM or neural network) on labeled swing data to classify swing quality, then use this model to provide real-time feedback during practice sessions.