Senlin Yang1 and Matthew Ngoi2, 1USA, 2California State Polytechnic University, USA
Golf is enjoyed by many individuals, however the high coaching costs hinder golfers, particularly those from lower-income backgrounds, from reaching their potential. This issue not only affects the golfers themselves but also impacts the growth and diversity of its community. To address this, I've developed the "GolfBud" app in which users can use their smartphones to record their swing and the app will use algorithms to generate comparisons with professional swings in the form of still images: providing personalized feedback for improvement. It has three main components, including a Flutter mobile interface, a server hosting the golf engine analyzer, and Firebase for image storage. The videos submitted by users are analyzed frame by frame, and get the body angles using OpenCV and Mediapipe [11][12]. These frames are grouped using K-means and then one frame is chosen from each group. Lastly, each of those frames are compared with similar frames from a professional swing. Three main challenges were figuring the methods to analyze golf swings, finding features to group the frames together and devising a way to allow easy access to the golf analyzer [14]. This innovative solution offers an affordable way to enhance a golfer's skills without having to pay the high coaching fee. It is affordable, accessible, and effective.
Golf, AI, Mobile Application, Swing analyser
