Athletic Performance Coaching via Neural Network Analysis
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Solution Overview
Problem
Conventional coaching methods for athletic pursuits and fitness require in-person consultations, limiting accessibility and efficiency in providing personalized feedback and improvement suggestions.
Innovation Solution
A system that uses image data from an athlete's movements to generate position data, which is applied to an artificial neural network trained on reference data sets to determine performance rankings and provide personalized coaching instructions for improved movements, considering fatigue and muscle fiber type analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If in-person consultation is used for coaching, then personalized feedback quality is improved, but accessibility and convenience deteriorate
Solution Approach 1:
The patent creates a digital copy of the coach's evaluation capability through an artificial neural network trained on reference movement data. The system captures image data of athlete movements, processes them through the trained ANN to generate position data, and compares against reference movements to provide automated feedback. This digital copy enables remote coaching with personalized feedback without requiring physical presence, thus resolving the contradiction between feedback quality and accessibility.
2Measurement precision
If manual coach evaluation is used, then feedback accuracy is improved, but time consumption and efficiency deteriorate
Solution Approach 1:
The patent replaces the mechanical system of manual coach evaluation with an automated computational system. The artificial neural network, trained on reference movement data, automatically analyzes captured image data, generates position information, and provides feedback without human intervention. This substitution maintains evaluation accuracy through systematic analysis while dramatically improving coaching efficiency and reducing time consumption, as the system can process multiple athletes simultaneously without fatigue or delays.
3Measurement precision
If comprehensive movement analysis is performed, then coaching precision is improved, but system complexity deteriorate
Solution Approach 1:
The patent segments the complex task of movement analysis into distinct functional components: image data capture, position data generation through neural network processing, reference movement comparison, and feedback generation. Each component handles a specific aspect of the analysis, allowing comprehensive performance evaluation while managing system complexity through modular architecture. The segmentation enables precise analysis without requiring a monolithic complex system.
Data Source
AI summary
An automated system provides for tracking and evaluation performance of an athlete. An athlete is tracked during performance of a movement, and position data of the performance is applied to an artificial neural network (ANN) trained via a reference data set representing recorded movements. Using the ANN, rank data for the performance is be determined, where the rank data indicates a relationship between the performance of the movement and a subset of the plurality of recorded movements. Based on the rank data, the athlete can be presented with an evaluation of the performance, instructions for subsequent movements and suggestions for improving the athlete's performance.