Analyzing Uncertainty of an Ankle Joint Model with Genetic Algorithm

Area of Science:

• Biomechanics
• Computational Biology
• Numerical Analysis

Background:

• Biomechanical models traditionally use fixed parameters.
• A paradigm shift suggests parameters should be treated as random variables, necessitating new numerical methods for uncertainty analysis.

Purpose of the Study:

• To introduce and verify a genetic algorithm for uncertainty analysis in biomechanical modeling.
• To apply this novel method to an ankle joint model with numerous parameters and flexible links.

Main Methods:

• Encoding two adversarial models within a single decision variable vector.
• Concurrently optimizing these models to maximize the difference between their outputs.
• Applying the procedure to an ankle joint model with 43 parameters and flexible links, with specified bounds for geometrical and material parameters.

Main Results:

• The genetic algorithm successfully analyzed uncertainty in the biomechanical model.
• Adversarial structures generated by the algorithm were visually indistinguishable.
• Despite visual similarity, the models exhibited up to 38.52% difference in angular displacements.

Conclusions:

• The proposed genetic algorithm offers a general and effective approach for biomechanical model uncertainty analysis.
• This method can identify significant parameter uncertainties even when model structures appear similar.
• Further research is needed to explore the computational efficiency and broader applicability of this technique.