Golf Club Fitting With Machine Learning From Swing Dynamics
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Solution Overview
Problem
Existing golf club fitting methods struggle to accurately determine the optimal golf clubs for a player based on their swing dynamics, leading to suboptimal performance, feel, and consistency.
Innovation Solution
A machine-learning-based system that utilizes a property prediction model to analyze golf swing data and compare it with predefined specifications of various golf clubs, determining optimal and pre-existing clubs that best match the user's characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional golf club fitting methods are used, then the process is simpler and faster, but the accuracy of determining optimal golf clubs is insufficient
Solution Approach 1:
The patent replaces traditional mechanical/manual club fitting methods with a machine learning-based system that uses sensors to capture swing data and algorithms to analyze it. The system substitutes human expert judgment with automated computational analysis, achieving higher measurement precision in determining optimal club specifications.
Solution Approach 2:
The patent introduces a machine learning model as an intermediary between the golfer's swing data and the club selection process. This intermediary processes complex swing dynamics data and translates it into recommendations for optimal club specifications, bridging the gap between raw data and actionable fitting decisions.
2Measurement precision
If machine learning analysis is applied to golf swing data, then the accuracy of club recommendations is improved, but the complexity of the system increases
Solution Approach 1:
The patent segments the club fitting process into distinct components: data collection through sensors, data processing through machine learning models, and recommendation generation. This segmentation allows the complex machine learning system to be broken down into manageable modules, making it easier to implement and maintain while achieving high accuracy.
Solution Approach 2:
The patent transforms physical swing parameters (club head speed, launch angle, spin rate) into digital data that can be processed by machine learning algorithms. By changing the representation of swing characteristics from physical measurements to computational parameters, the system achieves accurate analysis while managing complexity through standardized data formats.
3Reliability
If detailed swing data collection is performed, then the quality of fitting results is improved, but the time required for the fitting process increases
Solution Approach 1:
The patent performs preliminary actions by pre-training machine learning models with extensive swing data before actual club fitting sessions. This pre-processing of data and model preparation allows the system to quickly analyze individual golfer swings during the actual fitting process, reducing the time required while maintaining high quality results.
Solution Approach 2:
The patent enables continuous data collection during the golfer's natural swing practice, rather than requiring discrete measurement events. Sensors continuously capture swing data as the golfer practices, allowing the system to accumulate quality data without interrupting the fitting flow or requiring additional time for formal measurements.
Data Source
AI summary
Apparatus for a machine-learning-based golf club fitting platform is disclosed. An apparatus includes a processor and a memory. The memory stores code executable by the processor to receive a first set of golf swing data for a user, the first set of golf swing data representative of one or more characteristics of a golf swing of the user; determine one or more optimal specifications of at least one hypothetical golf club that is a best fit for the user using a property prediction machine learning model based on the first set of golf swing data; and determine at least one pre-existing golf club, comprising predefined specifications, that is a best match for the user based on a comparison of the determined one or more optimal specifications of the at least one hypothetical golf club to the predefined specifications of a plurality of different pre-existing golf clubs.