3D Motion Guidance With AI Feedback for Personalized Training
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Solution Overview
Problem
Existing motion capture and analysis systems lack the ability to create hyper-realistic digital avatars of instructors for personalized feedback and instruction, and the feedback provided is often not granular or tailored to the user's specific performance.
Innovation Solution
A closed-loop system that utilizes 3D motion capture, game engines, and AI-driven analysis to create hyper-realistic digital avatars of instructors, allowing for real-time performance assessment and automated, personalized feedback by comparing user movements to pre-recorded instructor data using neural networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If motion capture systems are used to provide feedback and control, then automated feedback can be delivered, but the ability to create hyper-realistic digital avatars and provide granular personalized feedback is lacking
Solution Approach 1:
The system segments the feedback into multiple granular components by analyzing specific body joints and movement segments separately. Each joint (shoulder, elbow, wrist, hip, knee, ankle) is evaluated independently against the instructor's reference motion, allowing for detailed, personalized feedback on each body part's performance while maintaining automated delivery.
2Manufacturing precision
If expert instructors manually provide feedback, then personalized and granular feedback can be delivered, but scalability is limited by geographical constraints and instructor availability
Solution Approach 1:
The system creates a digital copy (digital twin) of the expert instructor's movement patterns and teaching methodology through motion capture. This digital replica can be replicated infinitely without the constraints of physical presence, allowing the expert's knowledge to be scaled to unlimited students simultaneously while maintaining the same level of personalization and granularity as one-on-one instruction.
3Productivity
If motion capture data is captured and analyzed, then feedback can be provided, but the measurement precision from multiple angles and comprehensive evaluation is insufficient
Solution Approach 1:
The system transitions from 2D video analysis to 3D motion capture, adding the dimension of depth and spatial orientation. This enables precise measurement of movements from multiple angles simultaneously, capturing the complete three-dimensional trajectory of each body part relative to the instructor's reference motion, thereby significantly improving measurement precision without sacrificing feedback delivery speed.
Data Source
AI summary
The present invention relates to a motion capture and analysis method and system that provides automated, personalized feedback to users performing physical movements. The invention allows movement experience leaders to create hyper-realistic digital avatars of themselves, which guide users through specific movements and analyze their performance in real-time using advanced motion capture and AI techniques. By leveraging state-of-the-art game engines, neural networks, and 3D motion capture data, the system enables a highly immersive and interactive learning experience that closely mimics human-to-human instruction. The invention aims to provide a scalable platform for experts to deliver personalized training to a wide audience, with potential applications in virtual fitness classes, dance lessons, and sports coaching.