Athletic Skill Video Analysis for Personalized Biomechanic Feedback
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Solution Overview
Problem
Existing athletic performance analytics technologies lack precision and adaptability to cater to individual athletes' specific biomechanics, failing to provide real-time, actionable insights that accurately reflect personal strengths and weaknesses, thereby hindering optimal performance improvement.
Innovation Solution
Employing AI/ML to analyze a player's own shooting data, focusing on individual biomechanics and actual performance-of-skill success rates, and providing personalized biomechanic adjustments based on correlated metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional manual tracking methods are used to evaluate athletic performance, then the system complexity is low, but the measurement precision and reliability of biomechanic metrics are insufficient
Solution Approach 1:
The patent replaces manual mechanical tracking methods with automated computer vision and machine learning systems. Multiple cameras capture athletic performance data, and AI algorithms automatically analyze biomechanic metrics such as balance, alignment, and movement patterns, eliminating the need for manual observation and measurement while significantly improving precision.
Solution Approach 2:
The patent introduces AI software applications and processing systems as intermediaries between the athletic performance data and the evaluation results. These intermediaries automatically process video content, extract biomechanic metrics, and provide actionable insights, bridging the gap between raw data and meaningful performance assessment.
2Adaptability or versatility
If existing automated technologies are used, then the measurement precision is improved, but the adaptability to individual athlete biomechanics is insufficient
Solution Approach 1:
The patent applies local quality by customizing the evaluation criteria and biomechanic metrics for each individual athlete. The system analyzes each athlete's unique movement patterns, strengths, and weaknesses, providing personalized feedback rather than applying uniform standards to all athletes. This allows the system to adapt to individual biomechanics while maintaining high measurement precision.
Solution Approach 2:
The patent implements dynamic adaptability by allowing the system to learn and adjust to each athlete's evolving performance characteristics over time. The machine learning algorithms continuously refine their analysis based on accumulated data, enabling the system to adapt to changes in an athlete's biomechanics and provide increasingly accurate and personalized insights.
3Loss of information
If comprehensive biomechanic analysis is performed, then the measurement precision and actionable insights are improved, but the loss of time for data processing increases
Solution Approach 1:
The patent applies preliminary action by pre-configuring the system with relevant biomechanic metrics and evaluation criteria before data collection. The AI models are pre-trained to recognize and extract key performance indicators, allowing the system to process athletic performance data efficiently without requiring extensive post-processing or analysis setup, thus reducing time loss while maintaining comprehensive analysis quality.
4Reliability
If subjective observation methods are used, then the system complexity is low, but the reliability and consistency of performance evaluation deteriorates
Solution Approach 1:
The patent replaces subjective human observation with objective automated measurement systems. Computer vision technology and AI algorithms consistently evaluate biomechanic metrics without being influenced by human bias, fatigue, or variability, ensuring high reliability and consistency across all athletic performance assessments while managing system complexity through automated processing.
Data Source
AI summary
Aspects of the subject disclosure may include, for example, obtaining current video content of a player repeatedly performing a physical skill, analyzing the current video content based on previous video content, the previous video content comprises other video content of the player repeatedly performing the physical skill, determining biomechanic metrics of the player performing the physical skill based on the analysis, and determining each biomechanic metric of a portion of the biomechanic metrics does not satisfy a respective biomechanic metric success rate. Further embodiments include generating a first image of the player performing the physical skill from the current video content, generating a second image of the player performing the physical skill from the previous video content, and presenting the first image and the second image simultaneously and indicating the portion of the biomechanics that did not satisfy the respective biomechanic metric success rate. Other embodiments are disclosed.