Tyler Kaiyang Chen^1, and Yen-Hao Wang², ¹ USA, ²California State University, USA

This paper presents the design, implementation, and evaluation of a Golf Swing Analyzer, a low-cost, accessible system that delivers real-time feedback on golf swing mechanics [1]. Our system leverages MediaPipe for pose estimation, and a rule-based machine learning model training on labeled golf swing images to assess the swing based on parameters like elbow stability and shoulder posture. The backend, built with Python Flask, processes user inputs and runs swing analysis while the frontend provides an intuitive interface for ease of use [2]. To validate our approach, we conducted an experiment with 20 diverse swing images, which highlighted issues such as image blur, incorrect camera angles, and background distractions that impacted prediction accuracy. Compared to existing methods using expensive motion capture systems, or deep neural network-based analysis, our approach is faster, more accessible, and does not require expensive equipment or large training datasets. By improving accessibility and affordability, Perfect Pivot enables golfers of all skill levels to refine their swing, making golf coaching more inclusive and improving their technique more conveniently.

Golf Swing Analysis, Pose Estimation, Machine Learning, Real-Time Feedback