2015
DOI: 10.1016/j.jbiomech.2015.01.009
|View full text |Cite
|
Sign up to set email alerts
|

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

Abstract: 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-ω) and experimental voluntary neural drive-angular velocity (%VA-ω) datasets, obtained via the interpolated twitch techni… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1

Citation Types

0
3
0

Year Published

2018
2018
2024
2024

Publication Types

Select...
3
1

Relationship

1
3

Authors

Journals

citations
Cited by 4 publications
(3 citation statements)
references
References 16 publications
0
3
0
Order By: Relevance
“…The benchmark 17-parameter R T (ω, θ) function represented the H : Q fun increasing with angle and angular velocity as would be expected from both theory and experimental results. This function was based on a nine-parameter torque function that has been used multiple times to represent maximal voluntary joint torque as a function of angle and angular velocity with good results [39][40][41][42] and therefore it should be suitable to act (i) as a starting point for the derivation of a simpler ratio function, R E and (ii) as a benchmark against which the new function is tested.…”
Section: Discussionmentioning
confidence: 99%
See 1 more Smart Citation
“…The benchmark 17-parameter R T (ω, θ) function represented the H : Q fun increasing with angle and angular velocity as would be expected from both theory and experimental results. This function was based on a nine-parameter torque function that has been used multiple times to represent maximal voluntary joint torque as a function of angle and angular velocity with good results [39][40][41][42] and therefore it should be suitable to act (i) as a starting point for the derivation of a simpler ratio function, R E and (ii) as a benchmark against which the new function is tested.…”
Section: Discussionmentioning
confidence: 99%
“…Equation (2.1) is a nine-parameter function based on underlying muscle physiological properties that provides a three-dimensional description of the subject’s specific theoretical torque profile (figure 2 a , b ). This function has been used multiple times to represent the variation in maximal voluntary joint torque as a function of angular velocity and angle with good results [3942]. Tconctetfalse(ωfalse) and Tecctetfalse(ωfalse) are two rectangular hyperbolas representing tetanic force output as a function of contraction velocity in concentric ( ω ≥ 0) and eccentric ( ω < 0) contractions, respectively [40].…”
Section: Methodsmentioning
confidence: 99%
“…The individual pre-and post-training T-ω data sets for each subject were statistically compared by performing a nonlinear regression fit of the 7-parameter MVT function defined in Forrester et al (2011), first separately and subsequently to the combined pre and post-training data sets (Figure 2). The fits for each profile were statistically compared using the extra-sum-ofsquares F-test (Motulsky & Christopoulos, 2004;Voukelatos & Pain, 2015). The same statistical process was repeated for the %VA-ω data set by fitting a 3 rd degree polynomial to establish the training effect on voluntary activation (Figure 3).…”
Section: Phasementioning
confidence: 99%