Using individual-muscle specific instead of across-muscle mean data halves muscle simulation error

Abstract

Hill-type parameter values measured in experiments on single muscles show large across-muscle variation. Using individual-muscle specific values instead of the more standard approach of across-muscle means might therefore improve muscle model performance. We show here that using mean values increased simulation normalized RMS error in all tested motor nerve stimulation paradigms in both isotonic and isometric conditions, doubling mean simulation error from 9 to 18 (different at p < 0.0001). These data suggest muscle-specific measurement of Hill-type model parameters is necessary in work requiring highly accurate muscle model construction. Maximum muscle force (F _max) showed large (fourfold) across-muscle variation. To test the role of F _max in model performance we compared the errors of models using mean F _max and muscle-specific values for the other model parameters, and models using muscle-specific F _max values and mean values for the other model parameters. Using muscle-specific F _max values did not improve model performance compared to using mean values for all parameters, but using muscle-specific values for all parameters but F _max did (to an error of 14, different from muscle-specific, mean all parameters, and mean only F _max errors at p ≤ 0.014). Significantly improving model performance thus required muscle-specific values for at least a subset of parameters other than F _max, and best performance required muscle-specific values for this subset and F _max. Detailed consideration of model performance suggested that remaining model error likely stemmed from activation of both fast and slow motor neurons in our experiments and inadequate specification of model activation dynamics.