Real-Time Body Posture Flow Extraction for Interactive Training
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Solution Overview
Problem
Existing digital coaching and training applications for sports are either passive, providing only instructions or drilling plans, or function offline, requiring manual analysis of video recordings, and are complex and expensive for real-time analytics.
Innovation Solution
A system and method using a mobile device with a camera to provide interactive, real-time virtual coaching by receiving a training video, superimposing visual cues, extracting body posture flow using computer vision algorithms, determining player responses to cues, and generating feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If real-time analytics systems with multiple high-definition cameras are used, then measurement precision of player motion and form is improved, but device complexity and cost increase significantly
Solution Approach 1:
The patent extracts the essential function of motion tracking from complex multi-camera systems and implements it using a single mobile device camera. By isolating the core capability of capturing player motion and extracting body posture flow, the system achieves accurate tracking without requiring multiple high-definition cameras mounted on the game area.
Solution Approach 2:
The patent uses a mobile device camera to create a simplified copy of the functionality provided by expensive professional tracking systems. Instead of using multiple high-definition cameras with calibration equipment, the system captures video data from a single mobile device camera and processes it to extract body posture flow, achieving comparable tracking accuracy with much simpler hardware.
2Measurement precision
If real-time analytics systems with high-end hardware are used, then measurement precision of player motion is improved, but loss of energy and computational resources increase
Solution Approach 1:
The patent replaces expensive, resource-intensive high-end desktop and server-grade hardware with a mobile device that has limited but sufficient computational capabilities. The system processes video data from the mobile device camera using its own processor, avoiding the need for massive processing power while still achieving accurate body posture flow extraction and real-time analysis.
Solution Approach 2:
The patent changes the computational parameters by processing video frames sequentially and extracting body posture flow from individual frames rather than analyzing entire video streams simultaneously. This approach reduces the computational load on the mobile device while maintaining tracking accuracy, as the system processes one frame at a time using efficient algorithms.
3Measurement precision
If manual video analysis by coaches is used, then measurement precision of training performance is improved, but loss of time increases due to post-session analysis
Solution Approach 1:
The patent enables the system to perform self-service analysis by automatically extracting body posture flow from video data and generating training feedback without requiring manual intervention from coaches. The system independently processes video frames, identifies player movements, and provides real-time performance analysis, eliminating the need for time-consuming manual review while maintaining accurate measurement of training performance.
Solution Approach 2:
The patent implements real-time feedback by continuously analyzing body posture flow during the training session and providing immediate feedback to the player. Instead of waiting for post-session manual analysis, the system monitors training performance in real-time and delivers feedback during the exercise, significantly reducing the time loss associated with delayed analysis while maintaining measurement precision.
Data Source
AI summary
A computer implemented method for facilitating training of body-eye coordination using a computing device having access to a camera is disclosed. The method includes receiving a training video of a player from the camera; superimposing a visual cue onto the training video; extracting a body posture flow of the player from the training video by performing a computer vision algorithm on one or more frames of the training video; determining whether the player has responded to the visual cue by analyzing the body posture flow of the player; and generating a feedback to the player in response to determining that the player has responded to the visual cue. Multi-player embodiments of the present invention are also disclosed.