Twitch Interpolation Voluntary Activation
Calculate paired trial voluntary activation as 100·(1−superimposed/rest potentiated twitch); negative estimates are preserved to expose model/protocol incompatibility, not clipped.
Description
Twitch interpolation compares a stimulation-evoked increment during voluntary effort with a potentiated resting twitch.
Twitch interpolation compares a stimulation-evoked increment during voluntary effort with a potentiated resting twitch. The ratio is used to estimate voluntary activation.
The tool returns trialActivationPercents (trial activation percents); meanActivationPercent (mean activation percent). Input measurements and model parameters are supplied by the caller; the tool computes the stated summary rather than acquiring those measurements.
When to use Twitch Interpolation Voluntary Activation
Use Twitch Interpolation Voluntary Activation when you already have superimposedTwitchNewtons, potentiatedRestTwitchNewtons and want trialActivationPercents, meanActivationPercent. This is useful for checking a supplied calculation, documenting a measurement convention, or comparing observations prepared on the same basis.
How to use Twitch Interpolation Voluntary Activation and understand its results
- Enter superimposedTwitchNewtons, potentiatedRestTwitchNewtons using the field table below. Match the units, ordering and data boundaries described in the calculation.
- Run the tool after checking the stated bounds and matching paired observations or categories. A malformed request gives a tool error rather than a usable measurement.
- Read trialActivationPercents, meanActivationPercent. Use the calculation and interpretation below to distinguish amounts, ratios, signs and decision flags where applicable.
- Review the supplied parameters and assumptions before using the result in a report or comparing it with another observation.
| Input field | Meaning | Value type and bounds | Requirement |
|---|---|---|---|
| superimposedTwitchNewtons | Superimposed twitch newtons | list of number | Required |
| potentiatedRestTwitchNewtons | Potentiated rest twitch newtons | list of number | Required |
Calculate paired trial percentages from nonnegative superimposed and positive resting forces, then take their arithmetic mean.
activation% = 100 × (1 − superimposedTwitch/restingTwitch) The symbols refer to the supplied quantities or generated values described above; unit conversions and comparison boundaries follow the stated implementation.
{
"input": {
"superimposedTwitchNewtons": [
10,
20
],
"potentiatedRestTwitchNewtons": [
100,
100
]
},
"output": {
"trialActivationPercents": [
90,
80
],
"meanActivationPercent": 85
}
}Limitations and assumptions
Values are not clipped: a larger superimposed twitch yields negative activation. Measurement validity and physiological assumptions require separate review.
Numerical inputs must be finite. The tool checks its declared input shape and calculation constraints; those checks do not verify the quality, calibration or representativeness of the supplied observations.
Alternative or Complementary methods
Twitch Force: Measure sampled twitch peak and net impulse after baseline subtraction across explicit disjoint windows; force must already be calibrated. Muscle Force Quantification Tool: Fit a linear force-transducer calibration and apply it to recordings, preserving signed estimates outside calibration; no strength norms are embedded.
Methodological background: Twitch interpolation in human muscles: mechanisms and implications for measurement of voluntary activation.1
References
-
Twitch interpolation in human muscles: mechanisms and implications for measurement of voluntary activation — Herbert and Gandevia; Journal of Neurophysiology
Similar or alternative tools
- Twitch Force
Measure sampled twitch peak and net impulse after baseline subtraction across explicit disjoint windows; force must already be calibrated.
- Muscle Force Quantification Tool
Fit a linear force-transducer calibration and apply it to recordings, preserving signed estimates outside calibration; no strength norms are embedded.