Equine Facial Action Coding System for determination of pain-related facial responses in videos of horses.


Journal

PloS one
ISSN: 1932-6203
Titre abrégé: PLoS One
Pays: United States
ID NLM: 101285081

Informations de publication

Date de publication:
2020
Historique:
received: 25 03 2020
accepted: 29 09 2020
entrez: 3 11 2020
pubmed: 4 11 2020
medline: 31 12 2020
Statut: epublish

Résumé

During the last decade, a number of pain assessment tools based on facial expressions have been developed for horses. While all tools focus on moveable facial muscles related to the ears, eyes, nostrils, lips, and chin, results are difficult to compare due to differences in the research conditions, descriptions and methodologies. We used a Facial Action Coding System (FACS) modified for horses (EquiFACS) to code and analyse video recordings of acute short-term experimental pain (n = 6) and clinical cases expected to be in pain or without pain (n = 21). Statistical methods for analyses were a frequency based method adapted from human FACS approaches, and a novel method based on co-occurrence of facial actions in time slots of varying lengths. We describe for the first time changes in facial expressions using EquiFACS in video of horses with pain. The ear rotator (EAD104), nostril dilation (AD38) and lower face behaviours, particularly chin raiser (AU17), were found to be important pain indicators. The inner brow raiser (AU101) and eye white increase (AD1) had less consistent results across experimental and clinical data. Frequency statistics identified AUs, EADs and ADs that corresponded well to anatomical regions and facial expressions identified by previous horse pain research. The co-occurrence based method additionally identified lower face behaviors that were pain specific, but not frequent, and showed better generalization between experimental and clinical data. In particular, chewing (AD81) was found to be indicative of pain. Lastly, we identified increased frequency of half blink (AU47) as a new indicator of pain in the horses of this study.

Identifiants

pubmed: 33141852
doi: 10.1371/journal.pone.0231608
pii: PONE-D-20-08616
pmc: PMC7608869
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

e0231608

Déclaration de conflit d'intérêts

No competing interests.

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Auteurs

Maheen Rashid (M)

Dept. Computer Science, University of California Davis, Davis, California, United States of America.

Alina Silventoinen (A)

Dept. Anatomy, Physiology and Biochemistry, Swedish University of Agricultural Sciences, Uppsala, Sweden.

Karina Bech Gleerup (KB)

Dept. Clinical Sciences, University of Copenhagen, Taastrup, Denmark.

Pia Haubro Andersen (PH)

Dept. Clinical Sciences, Swedish University of Agricultural Sciences, Uppsala, Sweden.

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Classifications MeSH