paws: Pain Assessment at Withdrawal Speeds

Automated pain scoring from paw withdrawl tracking data based on Jones et al. (2020) A machine-vision approach for automated pain measurement at millisecond timescales. This R package takes paw trajectory data in response to a stimulus and provides an automated scoring of pain.

Installing from CRAN

install.packages("pawscore")

Installing from source

Using the devtools package (the first line can be skipped if devtools is already installed), in R run:

install.packages("devtools")
devtools::install_github("crtwomey/paws")

Compiling

To compile from the commandline, cd to the parent directory of the paws repository and run¹

$ R CMD build paws
$ R CMD install pawscore_1.0.2.tar.gz

Loading

Once installed, the package can be loaded into R using:

library(pawscore)

Usage

The package documentation explains the three main functions: extract_features, pain_score, and pain_class. A simple example is shown here using paw trajectory data from Jones et al., which is included with the package as jones2020.tracks.

# compute features for Jones et al. (2020) paw trajectories
paw.features <- lapply(jones2020.tracks, function(track) {
    extract_features(track$time.series)
})

# get strain information for each track
strains <- sapply(jones2020.tracks, function(track) track$strain)

# compute pain scores
scores <- pain_score(paw.features, strains=strains)

Plotting the kernel density estimates of score distributions by stimulus (excluding strain SJL; see the paper) reproduces Fig. 4I of Jones et al.

Diagnostics

It’s a good idea to check the output of the feature extraction process. Your experimental conditions may differ from those of Jones et al., and require different parameters. At a minimum, check that the time series of paw displacements returned by the extract_features method captures the window of paw activity (from the time when the paw first leaves the ground to when it last returns) and identifies the time of the first peak in paw height. This can be done as follows:

# using the first track from the Jones et al. (2020) data as an example
paw <- jones2020.tracks[[1]]$time.series

# extract features for this time series
features <- extract_features(paw)

# plotting the features will show the x (horizontal) and y (vertical)
# displacement of the paw over time, alongside the computed univariate
# projection used for estimating shaking and guarding sequences
plot(features)

The time of the first peak in vertical paw displacement is indicated by the red vertical line shown on each panel. The position of this peak can be checked in the second panel, which shows vertical displacement of the paw. The first panel shows horizontal displacement relative to the starting position of the paw. The time series of vertical paw displacements is shown using a linearly adjusted baseline that ensures the paw’s vertical position starts and ends at zero. The time (in frames) is relative to the estimated window of paw activity.

Note that extract_features assumes that displacement from the ground increases as y increases. If this is not true for your data because your tracking software uses a reference frame with the opposite orientation, you will need to flip your y axis data before passing it to extract_features. A simple way to do this is to pass max(y) - y instead.

More detailed information is available if requested when calling the extract_features function by including the diagnostics = TRUE parameter, as in the following example.

# extract features with diagnostic information
features <- extract_features(paw, diagnostics = TRUE)

# plot diagnostics for kinematic data (x and y displacements and velocity)
plot(features, panel = "kinematics")

Calling the plot function with diagnostic enriched extracted features will provide more detailed diagnostic plots. Including the panel = "kinematics" parameter in the above example will display panels for horizontal and vertical (x and y) displacements and estimated velocities. Here, frames are shown numbered identically to the original time series passed to extract_features, and the estimated window of paw activity is shown as dashed blue vertical bars. The time series can be clipped to just the window of activity by passing clipped = TRUE to the plot function (shown in the next example). The vertical displacement panel additionally shows estimated local peaks in paw height (the first of which determines \(t^\star\)), and the maximum height attained is indicated by a filled blue point.

If no panel or subset of panels is specified, then all diagnostic plots are shown, as in the next example.

# plot all diagnostics using the extracted paw features with diagnostics
# information from the previous example, clipped to the period of paw activity
plot(features, clipped = TRUE)

The clipped = TRUE property subsets the frames viewed in each panel according to the identified window of activity (the period between the blue dashed lines in the previous example). Other than this windowing, the first four panels are the same as in the previous example. The fifth panel shows the the univariate projection time series, as in the first example. The sixth and final panel shows this same univariate time series scaled by the maximum paw height attained, and annotated with periods after the time of the first peak, \(t^\star\), identified as either “guarding” (gray) or “shaking” (red).

In the sixth panel, counts are annotated above the panel for the estimated number of potential consecutive shakes in each period. Displacements above the shaking threshold (set by the shake.filter.threshold parameter; see below) are annotated by red points (these are points where displacement from previous local minima or maxima exceeds the threshold). Displacements below threshold are annotated by black points. Sequential runs of all below or all above threshold displacements are shown as regions bordered by black dashed vertical lines. A region is identified as shaking (colored red) if and only if at least two consecutive displacements are above threshold. This example shows one such shaking period with four “shakes”, three periods with no shakes above threshold (having 1, 2, and 2 consecutive displacements, respectively), and one period with only a single displacement above threshold.

Different diagnostic panels can be shown by passing one of the following arguments to the panel parameter of the plot function (by default all panels will be shown):

The first six options show a single panel in isolation. The remaining five options show some combination of panels, which can be useful when checking different parameter settings.

Parameters

There are a number of parameters associated with the feature extraction process. The defaults can be inspected and changed using the default_parameters and set_parameters functions, respectively, as shown below.

# returns the parameters used in Jones et al. 2020 (the default parameters used
# by the extract_parameters function)
default_parameters()

# an example of how to modify the default parameters (modifications are passed
# as arguments to set_parameters)
params <- set_parameters(fps = 1000, shake.filter.threshold = 0.4)

# the modified parameters can be passed to the extract_features function in
# place of the defaults
features <- extract_features(paw, parameters = params)

The default parameters assume that the x and y time series data passed to extract_features is in millimeters. If this is not the case, the window.threshold parameter should be scaled accordingly (this parameter is used for determing when the paw first lifts off the ground, and when it last returns to the ground). The list of parameters, their default values, units, and what they are used for, is given below, along with the most informative diagnostic panel for checking each value.

Many of these parameters can safely be held fixed across datasets (e.g. the filter order parameters). Others may need to be changed based on the characteristics of the noise in the tracking data, but are likely to have the same value as each other (e.g. global.peak.filter.size, local.peak.filter.size, and shake.filter.size are likely to all have the same value as each other, even if different from the default listed above). When modifying parameters, it will be best to start at the top of this list and work down to the bottom (the choice of parameters early in the list may affect later choices; less so vice versa).

Credit

Please cite Jones et al. (2020) and include a link to this repository if you use this code in an academic publication.

Jessica M Jones, William Foster, Colin R Twomey, Justin Burdge, Osama M Ahmed, Talmo D Pereira, Jessica A Wojick, Gregory Corder, Joshua B Plotkin, Ishmail Abdus-Saboor (2020) A machine-vision approach for automated pain measurement at millisecond timescales. eLife 9:e57258

Testing, bug reports, and code contributions very welcome.

Copyright (c) 2019 – 2023 Colin Twomey. Shared under a GNU GPLv3 license (see COPYING).