ArticlePloS one2022
Sharing pain: Using pain domain transfer for video recognition of low grade orthopedic pain in horses.
Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 21 citations in OpenAlex.
- Smartphone-Based Technologies in Equine Sports Medicine: Supporting Athlete Management-A Review.Sensors (Basel, Switzerland) · 2026Review
- Computational investigation of the social function of domestic cat facial signals.Scientific reports · 2024Article
- Automated acute pain prediction in domestic goats using deep learning-based models on video-recordings.Scientific reports · 2024Article
- From facial expressions to algorithms: a narrative review of animal pain recognition technologies.Frontiers in veterinary science · 2024Review
- Article
- Deep learning for video-based automated pain recognition in rabbits.Scientific reports · 2023Article
- Development, refinement, and validation of an equine musculoskeletal pain scale.Frontiers in pain research (Lausanne, Switzerland) · 2023Article
- Explainable automated recognition of emotional states from canine facial expressions: the case of positive anticipation and frustration.Scientific reports · 2022Article
- Facial Expressions of Horses Using Weighted Multivariate Statistics for Assessment of Subtle Local Pain Induced by Polylactide-Based Polymers Implanted Subcutaneously.Animals : an open access journal from MDPI · 2022Article
- Automated recognition of pain in cats.Scientific reports · 2022Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.