XR Sports Vision Training With Voice and Action Feedback
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Solution Overview
Problem
Athletes in team sports often fail to focus on all teammates and defensive players due to concentrating on close-range opponents, leading to disrupted tactics and mistakes, necessitating improved vision training systems.
Innovation Solution
A team sports vision training system using extended reality, voice interaction, and action recognition, comprising a head-mounted display, action capture device, and computing server, which generates and analyzes virtual scenarios and user actions to assess training effectiveness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If players focus on close-range teammates and defensive players, then they can monitor immediate threats and opportunities, but they ignore far-side teammates and defensive players causing tactics to fail
Solution Approach 1:
The system transitions from natural monocular/binocular vision to a multi-dimensional data representation by capturing player positions, velocities, and accelerations in 3D space using computer vision algorithms. This dimensional expansion allows the system to track and present far-side players that are naturally invisible to the athlete's direct line of sight.
Solution Approach 2:
The system introduces an intermediary computational layer (computer vision algorithms and processing modules) that captures, processes, and transforms raw video feeds into actionable visual information about far-side players. This intermediary system bridges the gap between the athlete's limited natural vision and the comprehensive tactical information needed.
2Ease of manufacture
If traditional vision training methods are used, then training can be conducted with simple equipment, but training effectiveness cannot be accurately measured and feedback is delayed
Solution Approach 1:
The system implements real-time feedback by continuously capturing athlete movements via computer vision, comparing them against ideal movement patterns, and providing immediate visual and auditory feedback. This closed-loop feedback system enables precise measurement of training effectiveness including reaction time, tracking accuracy, and decision-making quality.
Solution Approach 2:
The system replaces traditional mechanical measurement tools (stopwatches, manual observation) with computer vision-based optical measurement systems. These systems use algorithms to automatically track player positions, calculate velocities, and assess movement quality, providing objective and precise measurements without complex mechanical apparatus.
3Adaptability or versatility
If group training sessions are organized for vision training, then social learning and team coordination can be enhanced, but training costs and time requirements increase significantly
Solution Approach 1:
The system creates virtual copies of team training scenarios using computer-generated imagery and simulated player movements. Athletes can practice vision training individually by interacting with virtual teammates and opponents, replicating the tactical and coordination aspects of group training without requiring physical presence of multiple players. This copying approach maintains team coordination training benefits while eliminating the resource requirements of in-person group sessions.
Data Source
AI summary
A team sports vision training system based on extended reality, voice interaction and action recognition is configured to train vision and an action of a user. A head-mounted display device includes a task scenario player and a speech sensing module. An action capture device generates an action message. A computing server stores a scenario setting parameter group and includes a task scenario generating module, a speech recognition module and an action recognition module. The task scenario generating module generates a virtual task scenario image and a task parameter group according to the scenario setting parameter group. The speech recognition module generates a speech recognition result and a vision training result. Then action recognition module generates an action recognition result and a sport training result. The vision training result and the sport training result are configured to judge whether the user meets a training requirement.