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Sensitivity analysis guided improvement of an electromyogram-driven lumped parameter musculoskeletal hand model
Robert Hinson1, Katherine Saul2, Derek Kamper1
1UNC-NC State Joint Department of Biomedical Engineering, North Carolina State University, Raleigh, NC 27695, United States; UNC-NC State Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States.
Optimizing EMG-driven musculoskeletal models for rehabilitation requires identifying key parameters. Sensitivity analysis revealed muscle moment arms and maximum isometric force are crucial, enabling a refined optimization strategy with improved reliability.
Area of Science:
- Biomechanics and Rehabilitation Engineering
- Human-Machine Interaction
- Computational Modeling
Background:
- EMG-driven neuromusculoskeletal models are vital for studying impairments and advancing rehabilitation robotics.
- Accurate kinematic predictions depend on optimizing model parameters, a significant challenge for clinical use.
Purpose of the Study:
- To identify key parameters influencing EMG-driven musculoskeletal model accuracy using sensitivity analysis.
- To develop a refined optimization strategy for improved clinical application of these models.
Main Methods:
- Monte Carlo simulations were employed to assess parameter sensitivities in a lumped-parameter musculoskeletal model.
- Model predictions were compared against joint angle measurements from 11 able-bodied subjects.
- The study evaluated sensitivities for wrist and metacarpophalangeal (MCP) joint flexion/extension.
Main Results:
- Muscle moment arms, maximum isometric force, and tendon slack length were identified as highly influential parameters.
- Reducing the number of optimizable parameters from 22 to 14 did not significantly impact prediction accuracy.
- Wrist kinematic predictions were found to be independent of MCP muscle parameters.
Conclusions:
- Sensitivity analysis effectively guides the refinement of musculoskeletal models.
- A novel optimization strategy, informed by sensitivity analysis, significantly improved parameter identification reliability.
- These findings facilitate the clinical implementation of EMG-driven musculoskeletal models for rehabilitation.
