Adaptive Training System with Aerial Mobility and Optical Tracking
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Solution Overview
Problem
Existing athletic training systems fail to effectively identify and adapt to an athlete's weaknesses, limiting their ability to focus on improving specific skills, as they often restrict motion, lack automated adaptation, and are not suited for various athletic activities beyond linear speed and pacing.
Innovation Solution
An adaptive training system featuring a mobile unit with a control unit that receives athlete information, determines a training path, and adjusts its motion to stress weaknesses, using sensors and optical devices to monitor and interact with athletes, allowing for real-time feedback and dynamic path adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional training garments are used to track body movements, then performance measurement is achieved, but range of motion is restricted and utility decreases
Solution Approach 1:
The patent replaces mechanical body-worn tracking garments with an optical measurement system using cameras and computer vision technology. This substitution eliminates the mechanical constraints of physical sensors on clothing while maintaining measurement precision through digital image analysis and coordinate mapping.
2Speed
If simple track pacing devices are used, then linear speed training is achieved, but adaptability to various athletic activities is limited
Solution Approach 1:
The system provides universal training capability across multiple sports by using aerial mobile units that can execute diverse flight patterns and trajectories. The same platform can train linear speed, lateral agility, cutting movements, and positioning for different sports like football, basketball, and soccer through programmable motion paths and real-time tracking.
Solution Approach 2:
The patent transitions from ground-based linear track pacing to three-dimensional aerial mobility. The mobile units can move in multiple spatial dimensions and trajectories, enabling training of complex athletic movements including lateral translations, cutting patterns, and multi-directional sprints that cannot be modeled on simple linear tracks.
3Device complexity
If manual training program design is used, then training structure is established, but automated adaptation to athlete weaknesses is lacking
Solution Approach 1:
The system implements automated feedback loops where the control unit continuously receives performance data from optical tracking, analyzes athlete weaknesses in specific athletic skills, and automatically adjusts training paths and parameters. This closed-loop system adapts training programs in real-time based on measured performance without manual intervention.
Solution Approach 2:
The training system performs self-analysis and self-adjustment by automatically evaluating athlete performance data, identifying weaknesses, and modifying training parameters. The control unit autonomously manages the adaptation process, eliminating the need for manual program redesign by coaches.
Data Source
AI summary
A mobile unit configured to train an athlete is disclosed. The mobile unit includes multiple sensors, communication devices and a mobility system. The mobile unit executes one or more training paths to simulate chasing associated with various sports. The mobile unit is capable of determining its own location and the location of the athlete throughout a training session, as well as other information. The mobile unit is configured to adapt the training path to stress weaknesses of the athlete with respect to various types of athletic skills.