Sports Fitting Bay Using Machine Learning for Performance Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current sports training technologies lack efficient methods for analyzing user interactions with sports equipment, such as soccer cleats and soccer balls, to provide personalized performance metrics and equipment recommendations.
Innovation Solution
A system that includes a terminal with memory devices and processors, which captures user interactions using a camera, applies Machine Learning models to identify aspects of the interactions, and generates performance metrics, allowing for real-time feedback and equipment recommendations based on user performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional sports training methods are used, then training can be performed, but there is no efficient method for analyzing user interactions with sports equipment to provide personalized performance metrics
Solution Approach 1:
The patent replaces manual observation and mechanical measurement methods with an automated computer vision system using cameras and machine learning algorithms. The system captures images of users interacting with sports equipment, processes them through trained models to identify interactions and extract performance metrics, and provides automated analysis without requiring complex manual measurement apparatus.
Solution Approach 2:
The system enables self-service performance analysis by automatically capturing, processing, and evaluating user interactions with sports equipment without requiring coaches or trainers to manually observe and record performance data. The machine learning model autonomously identifies interactions and generates personalized metrics.
2Adaptability or versatility
If comprehensive analysis of user interactions is implemented, then personalized performance metrics can be provided, but the system complexity increases
Solution Approach 1:
The patent employs machine learning models that can adapt to different sports equipment and interaction types by training on diverse datasets. The system adjusts its analysis parameters and detection criteria based on the specific equipment being used (e.g., soccer cleats, soccer balls) and the type of interaction, enabling comprehensive personalization without requiring separate specialized systems for each equipment type.
Data Source
AI summary
A system can include one or more memory devices that can store instructions. The instructions can, when executed by one or more processors, cause the one or more processors to receive an indication of a selection of a plurality of pieces of sports equipment, provide a first prompt to initiate execution of a plurality of interactions between a user and an object, receive a first set of data corresponding to the plurality of interactions identify one or more images associated with the plurality of interactions, execute a Machine Learning (ML) model to apply bounding boxes to one or more objects included in the one or more images, determine one or more aspects of the plurality of interactions, and generate a performance metric for the plurality of interactions.