Left/Right Asymmetry of the Time-Averaged Sensor Map

The classic asymmetry index (L - R) / (0.5 * (L + R)) * 100, computed on the per-sensor map rather than frame by frame. Negative values are right-biased, positive values left-biased, and zero is symmetric.

Usage

pr_calc_symmetry_map(
  trial,
  masks = NULL,
  statistic = "mean",
  denominator = c("grid", "loaded")
)

Arguments

trial

A pr_trial object.

masks

What to compare. NULL (default) uses the whole active grid, split at floor(grid_cols / 2) exactly as pr_mask_symmetry() does. A single pr_mask or logical matrix restricts that comparison to a region of interest, split at the same midline. A list of exactly two masks gives the two sides explicitly, left first.

statistic

Character. "mean" (default), "max" or "loaded"; see pr_calc_regional_map().

denominator

Character. "grid" (default) or "loaded"; see Details.

Value

A one-row tibble::tibble with columns statistic, denominator, n_left, n_right, left_value, right_value and asymmetry_pct.

Details

L and R are the mean of the per-sensor map over the left and right sensors of the region of interest. Because the map is taken first, the index describes where load sat over the whole recording, not where the loudest frame happened to be; it is the counterpart of the frozen pr_calc_symmetry_index(), which averages within frames.

denominator chooses what goes into each side's mean:

• "grid" (default) averages over every sensor of the side, counting never-loaded ones as zeros. This is the cohort definition: on a mat that is the region of interest, an unloaded cell is a real measurement.

• "loaded" averages only the sensors whose map value is above zero. That answers a different question — how hard the loaded cells were pressed, ignoring how many there were — and is usually much closer to zero, because a side that loses contact area keeps its intensity.

The index is undefined when both sides are zero; that case returns 0, as pr_calc_symmetry_index() does.

Nothing here forces the two sides to hold the same number of sensors. When they do not, part of the index is a property of the mask — see pr_mask_mirror_balance() and pr_symmetry_sensitivity().

See also

pr_calc_symmetry_index() for the frame-first version, pr_symmetry_sensitivity() to vary the mask.

Other regional symmetry functions: pr_calc_cop_masked(), pr_calc_regional_map(), pr_mask_mirror_balance(), pr_mask_rowbands(), pr_symmetry_sensitivity()

Examples

trial <- pr_example_trial("saddle_horse")
pr_calc_symmetry_map(trial)
#> # A tibble: 1 × 7
#>   statistic denominator n_left n_right left_value right_value asymmetry_pct
#>   <chr>     <chr>        <int>   <int>      <dbl>       <dbl>         <dbl>
#> 1 mean      grid           124     124       1.48        1.30          13.1

# Restricted to the cranial band only:
bands <- pr_mask_rowbands(trial$layout, sides = FALSE)
pr_calc_symmetry_map(trial, bands$cranial)
#> # A tibble: 1 × 7
#>   statistic denominator n_left n_right left_value right_value asymmetry_pct
#>   <chr>     <chr>        <int>   <int>      <dbl>       <dbl>         <dbl>
#> 1 mean      grid            36      36       1.77        1.55          13.4

# Intensity-only comparison, ignoring how much area each side loaded:
pr_calc_symmetry_map(trial, denominator = "loaded")$asymmetry_pct
#> [1] 13.07347