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Moti tion Analysis to Quanti tita tati tively Assess th the Le Level of of Pain in Anima mals Annabelle Eyler Hood College February 1, 2020 Eyler Motion Analysis to Quantitatively Assess the Level of Pain in Animals 1 2019 SURIEM

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Moti tion Analysis to Quanti tita tati tively Assess th the Le Level

  • f
  • f Pain in Anima

mals

Annabelle Eyler Hood College February 1, 2020

Eyler Motion Analysis to Quantitatively Assess the Level of Pain in Animals 1

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2019 SURIEM Program at Michigan State University

Program Director: Dr. Robert Bell, Michigan State University Project Investigator: Dr. Mark Reimers, Michigan State University Research Members: Storm Chin, Morehouse College Joyce Quon, California State University, Los Angeles

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Assessment of Pain

  • Different forms of pain in animals, like humans
  • Currently lacking descriptors of pain
  • Distinguishing different qualities of pain

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Pain Through Fluidity of Movement

  • Correlation between pain and movement characteristics
  • Evaluate pain by help of motion patterns and fluidity
  • Determine defining characteristics to quantify pain

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How Motion was Tracked

  • Computer vision software DeepLabCut
  • Deep convolutional neural network
  • Requires annotations and training time

§ Build and train top two layers of network

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Our Variables

  • Control and injured rats
  • Injured rats were denervated on front right paw

§ Phantom limb pain

  • Different tests

§ Open Field § Novel Object Recognition

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Accuracy Problems

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Labeling Problems

  • Baseline errors
  • Human error
  • Social definition difference between labelers
  • Set standards lacking details
  • DeepLabCut provided trajectories
  • Mislabeling
  • Difference in pixels per feature

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Frame Error Sample Calculation

  • Area occupied by rat: (30,95)
  • x-axis SE: 1.78 pixels, y-axis SE: 2.30 pixel

Using the Pythagorean Theorem: distance2 = 2.302 + 1.782 distance = 2.91 pixels

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Distance From Mean

For this specific frame example, the distance from the mean is 2.91 pixels. Overall average distance was 3 pixels from mean, which is a 4.31% pixel error

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Processing DeepLabCut Predictions

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Nose Left Ear Right Ear Rump

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Processing Raw Data

  • Problem: Inaccurate predictions from DeepLabCut
  • Possible Solutions

§ Remove incorrect frames § Check confidence of each frame § Data smoothing

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What is a Moving Average?

  • A series of averages
  • f different subsets
  • f the data frame
  • Varies depending on

window size

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Before Interpolation After Interpolation

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Motion Analysis

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Total Distance Traveled

  • Used DeepLabCut output csv file
  • Created code to calculate Euclidean distance of each

feature between two frames

  • Executed for all frames of a video, added up for total

distance

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Velocity, Acceleration, and Jerk

  • Each calculate the difference between previous

calculation for all frames in video

  • Velocity vector stores the difference between two given

distances of one feature

  • Jerk (derivative of acceleration) measures change in

acceleration through video, frame to frame

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Novel Object Recognition Code

  • Creates a radius using annotated corners
  • Flag frame if nose label enters any radius
  • Determines number of flagged frames, converts to

seconds

  • Returns time that rat was near any object

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Results

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Trends: Nose vs. rump and left vs. right ear

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Distances: Nose Left Ear Right Ear Rump

Distance by Feature

Control Injured, no treatment

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Nose Distance vs. Rump Distance

Trends:

  • Control- move body less than

nose

  • Injured- higher ratio of

movement of nose to rump compared to control Generally, the control rats have the similar ratio of nose to rump movement

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Future Work

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  • Rats reacting to visual stimuli
  • Pupil diameter with body motion
  • Recording brain activity with brain activity

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Brain, Body, and Pupil Activity

  • Similar peaks seen
  • Attempt to find correlation

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Future Work

  • Application of code created, ability to apply to mainstream

motion analysis

§ Accessible to other computer vision software programs

  • Data smoothing to study humans
  • Generalize to determine level of engagement in humans

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References

  • A. Mathis, P. Mamidanna, K. M. Cury, T. Abe, V. N. Murthy, M. W. Mathis, and M.
  • Bethge. DeepLabCut: markerless pose estimation of user-defined body parts

with deep learning. Nature Neuroscience, 21:1281-1289, 2018.

  • T. Nath, A. Mathis, A. C. Chen, A. Patel, M. Bethge, and M. W. Mathis. Using

DeepLabCut for 3D markerless pose estimation across species. Nature Protocols, 14: 2152-2176, 2019.

  • S. Piana, P. Alborno, R. Niewiadomski, M. Mancini, G. Volpe, and A. Camurri.

Movement Fluidity Analysis Based on Performance and Perception. CHI Extended Abstracts '16, 1629-1636, 2016.

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Acknowledgements

I would like to thank Michigan State University and their 2019 Summer Undergraduate Research Institute in Experimental Mathematics (SURIEM) Program for hosting me for this research experience, which was financially supported by the National Science Foundation Award #1852066 and the National Security Agency Grant #H98230-1-0014. I would also like to thank Hood College, my home institution for the support in being here.

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