Digital Twin for Human Performance Prediction
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Solution Overview
Problem
Current methods for monitoring human performance are inadequate as they fail to account for the interrelated and synergistic effects of complex traits, leading to suboptimal performance and increased risk of injury, as they rely on a limited number of variables analyzed univariately, missing subtle yet critical patterns until it's too late.
Innovation Solution
A digital twin system is created using individualized neuromusculoskeletal modeling, non-invasive metabolic state monitoring, and physics-based modeling, combined with machine learning, to provide a comprehensive digital representation of an individual, integrating various data types including wearables, biomarkers, and medical imaging, allowing for dynamic simulation and personalized feedback on training, nutrition, and recovery protocols.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional performance monitoring measures a relatively small number of variables and compares them to population norms, then the approach is straightforward and easy to implement, but it fails to detect subtle yet suboptimal trait patterns and does not account for interrelatedness of traits, leading to undetected performance decline and increased injury risk
Solution Approach 1:
The patent combines multiple monitoring approaches (wearable sensors, medical imaging, genetic testing, metabolic monitoring) into an integrated multivariate system that simultaneously tracks numerous traits and their interactions, resolving the contradiction by merging simplicity of conventional methods with comprehensive detection capability
Solution Approach 2:
The digital twin system serves multiple functions: it monitors current performance, predicts future outcomes, identifies injury risks, and provides personalized recommendations, thereby achieving high measurement precision while maintaining ease of operation through a unified multi-functional platform
2Measurement precision
If a comprehensive digital twin system integrates individualized neuromusculoskeletal modeling, non-invasive metabolic state monitoring, full or partial body medical imaging, and machine learning, then measurement precision and detection capability are significantly improved, but the device complexity and data processing requirements increase substantially
Solution Approach 1:
The digital twin acts as an intermediary computational model that processes complex multivariate data from multiple sources, transforming raw data into actionable insights while managing system complexity through standardized data integration protocols and modular architecture
Solution Approach 2:
The system creates a virtual digital copy (twin) of the individual's physiological and performance characteristics, allowing comprehensive analysis of complex traits and their interactions without requiring direct manipulation of the actual biological system, thereby managing complexity through simulation
3Loss of time
If conventional methods use univariate analysis of selected variables, then the analysis is simple and quick, but it misses the synergistic effects of the system as a whole and fails to detect subtle trait patterns until it is too late
Solution Approach 1:
The digital twin system performs preliminary multivariate analysis and predictive modeling before performance failure or injury occurs, identifying subtle trait patterns and synergistic effects in advance, allowing preventive intervention while managing analysis complexity through automated computational algorithms
Data Source
AI summary
A method of generating a digital twin and of using the digital twin to predict activity of an animate subject. The digital twin is generated from at least system model data and movement data. The digital twin can be activated to simulate a specified activity that the subject is performing or will perform. If desired, the subject can be instructed to perform the same activity while wearing at least one wearable sensor, which is applied to the digital twin. Using artificial intelligence techniques, the activity simulation predicts one or more physical outcomes from the activity.