Predictive Finite Element Model for Combined Tendon Risk Factors
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Solution Overview
Problem
Current methods fail to effectively predict and mitigate repetitive stress injuries to soft tissues like tendons, particularly the supraspinatus tendon, due to a lack of integrated models that consider the interaction of posture, force, repetition, and duration, leading to insufficient ergonomic guidelines for injury prevention.
Innovation Solution
A predictive finite element model combining S-N curves and repetitive stress data sets to identify damage regimes and generate personalized guidelines for reducing tendon injury risk, incorporating factors such as posture, force, and repetition, using advanced technologies like ultrasound and finite element modeling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple ergonomic risk factors (posture, force, repetition, duration) are considered separately in current methods, then individual risk assessments can be made, but the interaction effects between these factors cannot be predicted and comprehensive injury risk assessment is insufficient
Solution Approach 1:
The patent combines multiple separate ergonomic risk factors (posture, force, repetition, duration) into a single integrated finite element model that predicts tendon damage accumulation. This merging allows the model to capture interaction effects between risk factors that separate assessments miss, thereby improving injury risk prediction accuracy while managing complexity through a unified computational framework.
Solution Approach 2:
The patent creates a composite predictive model that integrates data from multiple fields including materials science, ergonomics, medicine, and engineering. This composite approach combines S-N curves from materials science with repetitive stress data from ergonomics to form a comprehensive injury prediction model that leverages the strengths of each discipline.
2Reliability
If comprehensive models integrating multiple fields (materials science, ergonomics, medicine, engineering) are developed, then accurate injury prediction and personalized guidelines can be generated, but the complexity of creating and implementing such models increases significantly
Solution Approach 1:
The patent develops a multi-functional finite element model that serves multiple purposes: predicting tendon damage accumulation, identifying damage regimes, generating personalized ergonomic guidelines, and informing design requirements for artificial tendons. This universal model approach improves reliability by addressing multiple needs simultaneously while avoiding the complexity of developing separate models for each function.
Solution Approach 2:
The patent utilizes parameter changes in the finite element model to represent different ergonomic risk factors and their interactions. By varying parameters such as posture angles, force magnitudes, repetition frequencies, and duration, the model can predict damage accumulation under different working conditions and generate optimized guidelines without requiring fundamentally different model structures.
Data Source
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AI summary
A method for modeling soft tissue includes receiving one or more images showing an anatomical geometry of a first subject. The anatomical geometry includes a soft tissue. The method also includes measuring a plurality of parameters of the anatomical geometry of the first subject using one or more sensors attached to the first subject. The method also includes receiving a first set of material properties for the soft tissue of the first subject, a second subject, or both. The method also includes identifying a second set of material properties that characterizes the soft tissue while the first subject performs a task. The method also includes determining a strain on the soft tissue, a stress on the soft tissue, or both based at least partially upon the one or more images, the parameters, the first set of material properties, and the second set of material properties.