Surround-Perspective Motion Annotation for Personalized Sports Coaching
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Solution Overview
Problem
Current sports-coaching tools lack the analytical capabilities to determine a unique optimal motion signature for each player, relying on generic 'textbook' forms that fail to account for individual variations, leading to suboptimal performance.
Innovation Solution
A sports coaching platform utilizing surround-annotation motion analysis (SAM) and color-graded visual analysis to derive an optimal motion signature (OMS) by capturing and parsing video from multiple perspectives, incorporating vector generation, annotation, and physiological data for personalized coaching.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional textbook forms are used for coaching, then ease of operation is improved, but measurement precision deteriorates
Solution Approach 1:
The system creates digital copies of player motion through video capture and computer vision technology. Multiple video feeds are captured simultaneously, and AI algorithms generate skeletal models and motion vectors that replicate actual player movements. This allows precise measurement of motion parameters without requiring coaches to manually measure or memorize textbook forms.
Solution Approach 2:
The patent replaces manual coaching methods with automated computer vision and AI analysis systems. Instead of coaches physically observing and comparing player movements against textbook forms, the system uses multiple cameras, deep learning models, and automated annotation to objectively measure and analyze motion parameters, substituting mechanical human judgment with computational analysis.
2Device complexity
If generic textbook forms are applied to all players, then device complexity is reduced, but adaptability deteriorates
Solution Approach 1:
The system transitions from static textbook forms to dynamic, adaptive motion analysis. The AI models continuously learn from captured player movements and generate personalized motion signatures that adapt to each player's unique characteristics. The system dynamically adjusts analysis parameters and compares against player-specific baselines rather than fixed textbook standards, enabling personalization without proportionally increasing system complexity.
Solution Approach 2:
The patent changes the fundamental parameters of motion analysis from generic textbook specifications to player-specific measured parameters. Instead of comparing players to fixed form standards, the system captures actual motion parameters (joint angles, velocities, accelerations) and uses these as the basis for analysis and improvement, allowing each player to be evaluated on their own unique motion characteristics.
3Measurement precision
If multiple video perspectives are captured and analyzed, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system merges multiple video feeds and data sources into a unified analysis framework. Multiple cameras capturing different perspectives are integrated through synchronized timestamping and coordinate system transformation. The AI model combines skeletal data from multiple viewpoints, motion vectors, and physiological sensor data into a comprehensive motion signature, achieving high measurement precision through data fusion rather than complex individual analysis systems.
Solution Approach 2:
The patent implements a universal analysis platform that handles multiple video perspectives, sensor inputs, and analysis tasks through a single multi-functional system. The same deep learning model processes both skeletal extraction and motion parameter calculation from multiple camera feeds. This universal approach allows the system to analyze motion from any perspective using the same core algorithms, reducing overall system complexity compared to having separate specialized systems for each function.
Data Source
AI summary
A system and method is disclosed for a surround-perspective motion annotation comprising: an image/video input for capturing and/or parsing into at least one image frame of a subject performing at least one motion from each of at least three perspectives; a vector generating module for generating a test vector or wire mesh corresponding to a pose of the subject in each of the captured/parsed frame from each of the perspectives; and an annotation module for inputting a drawing imposed on the test vector/mesh for a visual contrast against any one of a feature from the test vector/mesh from any one of the perspectives. Further disclosed embodiments include for a system and method for generating multi-perspective, color-coded deviations of a subject from a reference (color wheel). Further embodiments include for a system and method for generating an optimal motion signature (OMS) for the subject based on his or her generated color wheel.