Myobolica: A Stochastic Approach to Estimate Physiological Muscle Control Variability

Abstract

The inherent redundancy of the musculoskeletal systems is traditionally solved by optimizing a cost function. This approach may not be correct to model non-adult or pathological populations likely to adopt a “non-optimal” motor control strategy. Over the years, various methods have been developed to address this limitation, such as the stochastic approach. A well-known implementation of this approach, Metabolica, samples a wide number of plausible solutions instead of searching for a single one, leveraging Bayesian statistics and Markov Chain Monte Carlo algorithm, yet allowing muscles to abruptly change their activation levels. To overcome this and other limitations, we developed a new implementation of the stochastic approach (Myobolica), adding constraints and parameters to ensure the identification of physiological solutions. The aim of this study was to evaluate Myobolica, and quantify the differences in terms of width of the solution band (muscle control variability) compared to Metabolica. To this end, both muscle forces and knee joint force solutions bands estimated by the two approaches were compared to one another, and against (i) the solution identified by static optimization and (ii) experimentally measured knee joint forces. The use of Myobolica led to a marked narrowing of the solution band compared to Metabolica. Furthermore, the Myobolica solutions well correlated with the experimental data (R $^{{2}} = 0.92$ , RMSE = 0.3 BW), but not as much with the optimal solution (R $^{{2}} = 0.82$ , RMSE = 0.63 BW). Additional analyses are required to confirm the findings and further improve this implementation.

Keywords

• Suboptimal control
• muscle recruitment
• stochastic approach
• Markov chain Monte Carlo
• OpenSim