Digital Quantum Twin Model for Human Movement Analysis
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Solution Overview
Problem
Current human movement analysis systems are inaccurate due to reliance on abstracted data, limited biomechanical modeling, and lack of individualized assessments, leading to potential injuries and suboptimal performance, especially in sports and health applications where precise movement analysis is critical.
Innovation Solution
Development of a digital quantum twin model of the human body that integrates clinical, physical, and biomechanical characteristics using markerless computer vision and machine learning, enabling accurate tracking and analysis of human movements in dynamic scenes, including the integration of forces and mass distribution, to create a subject-specific movement module.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional abstracted motion capture systems are used, then device complexity is reduced, but measurement precision deteriorates
Solution Approach 1:
The human body is segmented into multiple rigid body segments (head, torso, limbs, etc.) with defined joints and degrees of freedom. This segmentation allows the complex movement analysis to be broken down into manageable modular components, enabling high measurement precision through detailed tracking of individual segments while maintaining systematic organization that controls overall complexity.
Solution Approach 2:
A digital twin model acts as an intermediary between raw markerless motion capture data and meaningful movement analysis. This virtual human model serves as a mediator that transforms abstracted 2D/3D joint locations into anatomically accurate 6DoF segment orientations and positions, resolving the contradiction by providing a computational bridge that enhances precision without requiring direct complex physical measurement systems.
2Measurement precision
If generic motion capture models are used, then ease of operation is improved, but measurement precision deteriorates
Solution Approach 1:
The system implements local quality by creating subject-specific digital twin models that incorporate individual anatomical characteristics, body segment parameters, and personal movement patterns. Instead of applying a uniform generic model to all users, each person receives a customized model tailored to their specific biomechanics, thereby achieving high individualized assessment accuracy while the automated fitting process maintains ease of operation.
Solution Approach 2:
The system automatically adjusts key parameters of the digital twin model (body segment lengths, masses, inertias, joint ranges) based on individual subject data such as height, weight, age, and gender. This parameter customization enables precise individualized movement assessment without requiring manual system setup, as the parameters are automatically adapted to match each subject's unique characteristics.
3Measurement precision
If marker-based motion capture systems are used, then measurement precision is improved, but ease of operation deteriorates
Solution Approach 1:
The system replaces the mechanical marker-based measurement system with a markerless computer vision approach. Instead of physically attaching markers to the body and using optical tracking systems, the solution uses AI-driven pose estimation algorithms that extract movement data directly from video images. This substitution maintains measurement precision through advanced image processing while dramatically improving ease of operation by eliminating invasive marker attachment procedures.
Data Source
AI summary
The invention relates to systems and methods for the capture, tracking, analysis and assessment of human movements, action or behavior using novel digital quantum twin model of the human. The model is integrated and reinforced by novel representations and novel techniques in computer vision, machine learning, speech processing, sport science, exercise and health. The aim is to achieve optimal analysis and assessment of human motion and other impacting internal and external forces using valid quantum physics-based model of the human combining movements, behaviors, and other health info.Unlike existing approaches derived from two- or three-dimensional landmarks or just the shape or composition e.g. those extracted from images, videos or sensors, this invention develops an accurate finite element-like quantum representations of human-specific body combining shape features, anatomical structure, internal particles, their intensity, classifications and is constraint by clinical, physical, and biomechanical characteristics of the body and forces affecting each particle.