Biomechanical Interface Design Using Subject-Specific FEA
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Solution Overview
Problem
Current prosthetic socket design for lower extremity amputees is largely manual and non-data-driven, leading to inconsistencies in fit and comfort, resulting in discomfort, skin problems, and increased risk of pressure ulcers due to inadequate load distribution and tissue deformation.
Innovation Solution
A data-driven, automated computational framework that uses non-invasive imaging and finite element analysis (FEA) to create subject-specific biomechanical interfaces, accounting for tissue geometry and material properties to optimize socket design and reduce user involvement through iterative virtual prototyping.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual artisanal methods are used for socket design, then the process allows for expert judgment and customization, but the design is not repeatable and lacks quantitative data-driven optimization
Solution Approach 1:
The patent replaces manual artisanal methods with an automated computational framework that uses non-invasive imaging (MRI, CT, ultrasound) and finite element analysis (FEA) to objectively quantify tissue geometry and mechanical properties. This substitution transforms subjective expert judgment into objective, repeatable, data-driven design parameters while maintaining the ability to customize interfaces for individual subjects.
Solution Approach 2:
The patent creates accurate digital copies of the subject's body segment geometry through non-invasive imaging techniques. These digital models serve as precise replicas that can be repeatedly analyzed and used for virtual prototyping, eliminating the need for physical casts and enabling consistent, repeatable design processes while preserving subject-specific anatomical details.
2Manufacturing precision
If iterative physical prototyping and testing are used, then design optimization can be achieved through subject feedback, but the process is time-consuming and requires repeated subject involvement
Solution Approach 1:
The patent performs preliminary virtual prototyping and optimization using finite element analysis before manufacturing any physical prototypes. The computational framework simulates various design iterations and predicts their performance on the digital twin, allowing design optimization to be completed in silico. This preliminary virtual testing eliminates the need for multiple rounds of physical prototyping and subject testing, significantly reducing time loss while achieving the same optimization goals.
3Strength
If rigid materials are used for prosthetic interfaces, then structural strength is maintained, but tissue deformation thresholds may be exceeded causing pressure ulcers and discomfort
Solution Approach 1:
The patent applies local quality by spatially varying the mechanical properties of the prosthetic interface materials. Using finite element analysis, the system identifies regions of high stress concentration and tissue vulnerability, then prescribes localized areas of reduced stiffness or increased compliance in the interface design. This allows the structure to maintain overall strength while providing localized pressure relief in vulnerable regions, preventing tissue damage without compromising structural integrity.
Solution Approach 2:
The patent changes material parameters (stiffness, compliance, elasticity) based on quantitative tissue property measurements and FEA results. The system adjusts these parameters spatially and temporally to optimize the balance between structural strength and tissue protection, transforming the interface from a uniform rigid structure to a dynamically adapted system that responds to local tissue conditions.
4Manufacturing precision
If subject-specific custom designs are manufactured, then fit and comfort are optimized, but the manufacturing process becomes complex and costly
Solution Approach 1:
The patent replaces complex manual customization processes with automated computational design and manufacturing systems. The framework uses non-invasive imaging data and FEA to automatically generate optimized interface geometries and material distributions, then guides additive manufacturing or other automated fabrication processes. This substitution reduces manufacturing complexity by eliminating manual measurement, modeling, and iteration steps while maintaining high fit quality through precise digital-to-physical manufacturing workflows.
Data Source
AI summary
A method and associated system for designing a biomechanical interface of a device contacting a biological body segment of a subject includes forming a quantitative model of the biological body segment from subject specific data, conducting a biophysical analysis, such as a finite element analysis, to thereby establish a relationship, such as a functional relationship, between the quantitative model and at least one feature of the biomechanical interface contacting the biological body segment, and applying the relationship to the at least one feature of the biomechanical interface contacting the biological body segment to thereby obtain an interface design for the mechanical interface of the device. The subject-specific data can include geometry of the biological body segment and the at least one feature can be associated with physiological benefit of the biological body segment.