Compute work and power output from a work loop experiment on a per-cycle basis.

analyze_workloop(x, simplify = FALSE, GR = 1, M = -1,
  vel_bf = 0.05, ...)

Arguments

x

A workloop object of class muscle_stim that has been passed through select_cycles. See Details.

simplify

Logical. If FALSE, the full analyzed workloop object is returned. If TRUE a simpler table of net work and power (by cycle) is returned.

GR

Gear ratio, set to 1 by default

M

Velocity multiplier, set adjust the sign of velocity. This parameter should generally be either -1 (the default) or 1.

vel_bf

Critical frequency (scalar) for low-pass filtering of velocity via signal::butter()

...

Additional arguments potentially passed down from read_analyze_wl() or read_analyze_wl_dir()

Value

The function returns a list of class analyzed_workloop that provides instantaneous velocity, a smoothed velocity, and computes work, instantaneous power, and net power from a work loop experiment. All data are organized by the cycle number and important metadata are stored as Attributes.

Within the list, each entry is labeled by cycle and includes:

Time

Time, in sec

Position

Length change of the muscle, corrected for gear ratio, in mm

Force

Force, corrected for gear ratio, in mN

Stim

When stimulation occurs, on a binary scale

Cycle

Cycle ID, as a letter

Inst_velocity

Instantaneous velocity, computed from Position change, reported in meters/sec

Filt_velocity

Instantaneous velocity, after low-pass filtering, again in meter/sec

Inst_Power

Instantaneous power, a product of Force and Filt_velocity, reported in J

Percent_of_Cycle

The percent of that particular cycle which has elapsed

In addition, the following information is stored in the analyzed_workloop object's attributes:
stimulus_frequency

Frequency at which stimulus pulses occurred

cycle_frequency

Frequency of oscillations (assuming sine wave trajectory)

total_cycles

Total number of oscillatory cycles (assuming sine wave trajectory) that the muscle experienced.

cycle_def

Specifies what part of the cycle is understood as the beginning and end. There are currently three options: 'lo' for L0-to-L0; 'p2p' for peak-to-peak; and 't2t' for trough-to-trough

amplitude

Amplitude of length change (assuming sine wave trajectory)

phase

Phase of the oscillatory cycle (in percent) at which stimulation occurred. Somewhat experimental, please use with caution

position_inverted

Logical; whether position inversion has been applied)

units

The units of measurement for each column in the object after running this function. See Warning

sample_frequency

Frequency at which samples were collected

header

Additional information from the header

units_table

Units from each Channel of the original ddf file

protocol_table

Protocol in tabular format; taken from the original ddf file

stim_table

Specific info on stimulus protocol; taken from the original ddf file

stimulus_pulses

Number of sequential pulses within a stimulation train

stimulus_offset

Timing offset at which stimulus began

gear_ratio

Gear ratio applied by this function

file_id

File name

mtime

Time at which file was last modified

retained_cycles

Which cycles were retained, as numerics

summary

Simple table showing work (in J) and net power (in W) for each cycle

Details

Please note that select_cycles() must be run on data prior to using this function. This function relies on the input muscle_stim object being organized by cycle number.

The muscle_stim object (x) must be a workloop, preferably read in by one of our data import functions. Please see documentation for as_muscle_stim() if you need to manually construct a muscle_stim object from a non .ddf source.

The gear ratio (GR) and velocity multiplier (M) parameters can help correct for issues related to the magnitude and sign of data collection. By default, they are set to apply no gear ratio adjustment and to positivize velocity. Instantaneous velocity is often noisy and the vel_bf parameter allows for low-pass filtering of velocity data. See signal::butter() and signal::filtfilt() for details of how filtering is achieved.

Please also be careful with units! Se Warning section below.

Warning

Most systems we have encountered record Position data in millimeters and Force in millinewtons, and therefore this function assumes data are recorded in those units. Through a series of internal conversions, this function computes velocity in meters/sec, work in Joules, and power in Watts. If your raw data do not originate in millimeters and millinewtons, please transform your data accordingly and ignore what you see in the attribute units.

References

Josephson RK. 1985. Mechanical Power output from Striated Muscle during Cyclic Contraction. Journal of Experimental Biology 114: 493-512.

See also

Examples

library(workloopR) # import the workloop.ddf file included in workloopR wl_dat <-read_ddf(system.file("extdata", "workloop.ddf", package = 'workloopR'), phase_from_peak = TRUE) # select cycles 3 through 5 via the peak-to-peak definition wl_selected <- select_cycles(wl_dat, cycle_def = "p2p", keep_cycles = 3:5) # run the analysis function and get the full object wl_analyzed <- analyze_workloop(wl_selected, GR = 2) # print methods give a short summary print(wl_analyzed)
#> File ID: workloop.ddf #> Cycles: 3 cycles kept out of 6 #> Mean Work: 0.00308 J #> Mean Power: 0.08474 W #>
# summary provides a bit more detail summary(wl_analyzed)
#> # Workloop Data: #> #> #> File ID: workloop.ddf #> Mod Time (mtime): 2020-09-16 11:25:43 #> Sample Frequency: 10000Hz #> #> data.frame Columns: #> Position (mm) #> Force (mN) #> Stim (TTL) #> Cycle (letters) #> Inst_Velocity (m/s) #> Filt_Velocity (m/s) #> Inst_Power (W) #> Percent_of_Cycle (NA) #> #> Stimulus Offset: 0.012s #> Stimulus Frequency: 300Hz #> Stimulus Width: 0.2ms #> Stimulus Pulses: 4 #> Gear Ratio: 2 #> #> Cycle Frequency: 28Hz #> Total Cycles (peak-to-peak): 6 #> Cycles Retained: 3 #> Amplitude: 1.575mm #> #> #> Cycle Work Net_Power #> a A 0.002785397 0.07639783 #> b B 0.003147250 0.08661014 #> c C 0.003305744 0.09122522
# run the analysis but get the simplified version wl_analyzed_simple <- analyze_workloop(wl_selected, simplify = TRUE, GR = 2)