Sharing pain: Using pain domain transfer for video recognition of low grade orthopedic pain in horses

PLoS One. 2022 Mar 4;17(3):e0263854. doi: 10.1371/journal.pone.0263854. eCollection 2022.

Abstract

Orthopedic disorders are common among horses, often leading to euthanasia, which often could have been avoided with earlier detection. These conditions often create varying degrees of subtle long-term pain. It is challenging to train a visual pain recognition method with video data depicting such pain, since the resulting pain behavior also is subtle, sparsely appearing, and varying, making it challenging for even an expert human labeller to provide accurate ground-truth for the data. We show that a model trained solely on a dataset of horses with acute experimental pain (where labeling is less ambiguous) can aid recognition of the more subtle displays of orthopedic pain. Moreover, we present a human expert baseline for the problem, as well as an extensive empirical study of various domain transfer methods and of what is detected by the pain recognition method trained on clean experimental pain in the orthopedic dataset. Finally, this is accompanied with a discussion around the challenges posed by real-world animal behavior datasets and how best practices can be established for similar fine-grained action recognition tasks. Our code is available at https://github.com/sofiabroome/painface-recognition.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Animals
  • Communications Media*
  • Horses
  • Pain / veterinary

Grants and funding

Sofia Broomé is funded by the Swedish Research Council (https://www.vr.se), grant number 2016-03967 (award received by H.K. and P.H.A). Katrina Ask is funded by the Swedish Research Council FORMAS (http://www.formas.se), grant number 2016-01760 (MR) (award received by P.H.A). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.