Clean tracks • animovement

library(animovement)
#> -- Attaching packages ------------------------------------- animovement 0.7.3 --
#> v aniframe   0.6.0     v anicheck   0.2.0
#> v aniread    0.5.0     v animetric  0.3.2
#> v anispace   0.1.3     v anivis     0.2.0
#> v aniprocess 0.2.0
library(tibble)
library(dplyr, warn.conflicts = FALSE)
library(here)
#> here() starts at /home/runner/work/animovement/animovement
here::i_am("vignettes/articles/clean-tracks.Rmd")
#> here() starts at /home/runner/work/animovement/animovement

The next step in our workflow is to clean the tracks. This step commonly covers three separate components:

Outlier detection
Interpolation
Smoothing

Although these are not always completely separate steps in practice, we will treat them as such to ensure the integrity of our tracks.

Interpolation

In case we removed any outliers, we can now interpolate across the gaps created.

TO BE CONTINUED…

Smoothing

All there is left to do is smooth our tracks, which is done using the smooth_tracks() function. The smoothing itself is super simple. smooth_tracks() provides a few different options:

roll_mean
roll_median
SOON savitsky_golay

For the rolling filters you can provide the window_width, i.e. how many observations to use in the rolling filter. The filters result in some NA values at the beginning and end of your data.

An important point about smooth_tracks() is that, instead of using our x and y values, it first back-transforms into the raw values obtained from your sensors (which are effectively “differences” between coordinates, so dx and dy) and performs the smoothing on them, before finally converting back to x and y. This may seem strange if you have previously worked with tracking data from computer vision or GPS loggers. However, whereas those modalities would return to the “true” coordinates after an outlier, mouse sensors do not. So the only way we can identify rogue values is by filtering those raw values.

Let’s try smoothing our data with a rolling_mean filter with 0.5 second (30 observations at at a sampling rate of 60Hz) window width. In case we work with multiple keypoints and/or individuals we can use group_by with our metadata for a tidyverse-friendly workflow.

df_rollmedian <- df |>
  filter_aniframe(method = "rollmedian", 
                  window_width = 3, 
                  use_derivatives = TRUE)
df_kalman <- df |>
  filter_aniframe(method = "kalman", 
                  sampling_rate = 60, 
                  use_derivatives = TRUE)
df_sgolay <- df |>
  filter_aniframe(method = "sgolay", 
                  sampling_rate = 60, 
                  use_derivatives = TRUE)
df_lowpass <- df |>
  filter_aniframe(method = "lowpass", 
                  cutoff_freq = 0.1, 
                  sampling_rate = 60, 
                  use_derivatives = TRUE)

Let’s visualise how they compare. Note that although the difference may seem negligible when plotting paths, they may become important when computing derivatives such as velocity and acceleration.

library(ggplot2)
ggplot() +
  geom_path(data = df, aes(x, y), colour = "red") +
  geom_path(data = df_rollmedian, aes(x, y), colour = "blue") +
  geom_path(data = df_kalman, aes(x, y), colour = "orange") +
  geom_path(data = df_sgolay, aes(x, y), colour = "purple") +
  geom_path(data = df_lowpass, aes(x, y), colour = "green")
#> Warning: Removed 1 row containing missing values or values outside the scale range
#> (`geom_path()`).
#> Warning: Removed 19 rows containing missing values or values outside the scale range
#> (`geom_path()`).

Not that different as the sensors are doing a good job! But we can see that the smoothed track end a bit further to the left than the raw version.