Modelling suppressed muscle activation by means of an exponential sigmoid function: Validation and bounds

作者：Voukelatos Dimitrios; Pain Matthew T G^*

来源：Journal of Biomechanics, 2015, 48(4): 712-715.

DOI：10.1016/j.jbiomech.2015.01.009

• Interpolate twitch
• Force
• Velocity
• Quadriceps

The aim of this study was to establish how well a three-parameter sigmoid exponential function, DIFACT, follows experimentally obtained voluntary neural activation-angular velocity profiles and how robust it is to perturbed levels of maximal activation. Six male volunteers (age 26.3 +/- 2.73 years) were tested before and after an 8-session, 3-week training protocol. Torque-angular velocity (T-omega) and experimental voluntary neural drive-angular velocity (%VA-omega)) datasets, obtained via the interpolated twitch technique, were determined from pre- and post-training testing sessions. Non-linear regression fits of the product of DIFACT and a Hill type tetanic torque function and of the DIFACT function only were performed on the pre- and post-training T-omega and %VA-omega datasets for three different values of the DIFACT upper bound, alpha(max), 100%, 95% & 90%. The determination coefficients, R-2, and the RMS of the fits were compared using a two way mixed ANOVA and results showed that there was no significant difference (p < 0.05) due to changing alpha(max) values indicating the DIFACT remains robust to changes in maximal activation. Mean R-2 values of 0.95 and 0.96 for pre- and post-training sessions show that the maximal voluntary torque function successfully reproduces the T-omega raw dataset.

• 出版日期2015-2-26