Machine Learning Golf Club Fitting via Optical Motion Tracking

The fitting system segments the club selection process into multiple independent analysis components: optical motion tracking data analysis, machine learning predictive algorithms, and multiple club specification parameters (shaft flex, length, grip size, lie angle, loft). Each component processes specific aspects of swing mechanics independently, then integrates results to provide comprehensive club recommendations that accurately account for individual golfer characteristics.

The patent introduces an optical motion tracking system as an intermediary device between the golfer and the club fitting process. This intermediary captures detailed swing mechanics data through optical sensors and cameras, translating physical swing movements into quantifiable metrics that feed into machine learning algorithms, thereby enabling precise measurement without directly altering the golfer's natural swing.

The system performs preliminary actions by pre-processing optical motion tracking data during the swing capture phase, organizing raw data into structured formats suitable for machine learning analysis. The machine learning models are pre-trained on extensive swing data sets before actual fitting sessions, enabling rapid inference during the actual fitting process. This preliminary preparation reduces real-time computational requirements and accelerates the delivery of personalized club recommendations.

The patent replaces traditional mechanical club fitting methods (manual observation, physical trial-and-error) with computational systems comprising machine learning algorithms and optical motion tracking. This substitution transforms the fitting process from a time-intensive manual procedure to an automated computational analysis that can process multiple swing parameters simultaneously and generate personalized recommendations more efficiently.