Sharing Pain: Using Pain Domain Transfer for Video Recognition of Low Grade Orthopedic Pain in Horses

TL;DR

This paper explores transferring knowledge from acute pain datasets to recognize subtle orthopedic pain in horses using video analysis, addressing challenges in labeling and variability of pain behaviors.

Contribution

It introduces a domain transfer approach for recognizing low-grade orthopedic pain in horses, leveraging models trained on acute pain data, and provides a comprehensive empirical evaluation.

Findings

Model trained on acute pain data improves orthopedic pain recognition.

Human expert baseline established for subtle pain detection.

Analysis of domain transfer methods and real-world dataset challenges.

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.