Calculate timing and magnitude of force at stimulation, peak force, and various parts of the rising (force development) and relaxation (falling) phases of the twitch.

isometric_timing(x, rising = c(10, 90), relaxing = c(90, 50))

Arguments

x

A muscle_stim object that contains data from an isometric twitch trial, ideally created via read_ddf.

rising

Set points of the rising phase to be described. By default: 10% and 90%.

relaxing

Set points of the relaxation phase to be described. By default: 90% and 50%.

Value

A data.frame with the following metrics as columns:

file_ID

File ID

time_stim

Time between beginning of data collection and when stimulation occurs

force_stim

Magnitude of force at the onset of stimulation

time_peak

Absolute time of peak force, i.e. time between beginning of data collection and when peak force occurs

force_peak

Magnitude of peak force

time_rising_X

Time between beginning of data collection and X% of force development

force_rising_X

Magnitude of force at X% of force development

time_relaxing_X

Time between beginning of data collection and X% of force relaxation

force_relaxing_X

Magnitude of force at X% of relaxation

Details

The data.frame (x) must have time series data organized in columns. Generally, it is preferred that you use a muscle_stim object imported by read_ddf().

The rising and relaxing arguments allow for the user to supply numeric vectors of any length. By default, these arguments are rising = c(10, 90) and relaxing = c(90, 50). Numbers in each of these correspond to percent values and capture time and force at that percent of the corresponding curve. These values can be replaced by those that the user specifies and do not necessarily need to have length = 2. But please note that 0 and 100 should not be used, e.g. rising = seq(10, 90, 5) works, but rising = seq(0, 100, 5) does not.

References

Ahn AN, and Full RJ. 2002. A motor and a brake: two leg extensor muscles acting at the same joint manage energy differently in a running insect. Journal of Experimental Biology 205, 379-389.

See also

Other data analyses: analyze_workloop, read_analyze_wl_dir, read_analyze_wl

Other twitch functions: fix_GR, invert_position

Examples

library(workloopR) # import the twitch.ddf file included in workloopR twitch_dat <-read_ddf(system.file("extdata", "twitch.ddf", package = 'workloopR')) # run isometric_timing() to get info on twitch kinetics # we'll use different set points than the defaults analyze_twitch <- isometric_timing(twitch_dat, rising = c(25, 50, 75), relaxing = c(75, 50, 25) ) # see the results analyze_twitch
#> file_id time_stim force_stim time_peak force_peak time_rising_25 #> 1 twitch.ddf 0.1002 224.067 0.1141 412.4495 0.1057 #> force_rising_25 time_rising_50 force_rising_50 time_rising_75 force_rising_75 #> 1 273.743 0.107 321 0.1087 366.1605 #> time_relaxing_75 force_relaxing_75 time_relaxing_50 force_relaxing_50 #> 1 0.1241 365.5155 0.1311 318.2585 #> time_relaxing_25 force_relaxing_25 #> 1 0.143 271.808