Ask, Katrina
- Department of Animal Biosciences, Swedish University of Agricultural Sciences
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.
Published in
PLoS ONE
2022, volume: 17, number: 3, article number: e0263854
Publisher: PUBLIC LIBRARY SCIENCE
SLU Authors
Haubro Andersen, Pia
- Department of Clinical Sciences, Swedish University of Agricultural Sciences
- Department of Clinical Sciences, Swedish University of Agricultural Sciences
UKÄ Subject classification
Clinical Science
Publication identifier
- DOI: https://doi.org/10.1371/journal.pone.0263854
Permanent link to this page (URI)
https://res.slu.se/id/publ/117431