Skip to main content

REVIEW article

Front. Vet. Sci.
Sec. Anesthesiology and Animal Pain Management
Volume 11 - 2024 | doi: 10.3389/fvets.2024.1436795
This article is part of the Research Topic Pain Assessment and Management in Veterinary Medicine View all 5 articles

From Facial Expressions to Algorithms: A Narrative Review of Animal Pain Recognition Technologies

Provisionally accepted
Ludovica Chiavaccini Ludovica Chiavaccini 1*Anjali Gupta Anjali Gupta 1Guido Chiavaccini Guido Chiavaccini 2
  • 1 University of Florida, Gainesville, United States
  • 2 Independent researcher, Livorno, Italy

The final, formatted version of the article will be published soon.

    Facial expressions are essential for communication and emotional expression across species. Despite the improvements brought by tools like the Horse Grimace Scale (HGS) in pain recognition in horses, their reliance on human identification of characteristic traits presents drawbacks such as subjectivity, training requirements, costs, and potential bias. Despite these challenges, the development of facial expression pain scales for animals has been making strides. To address these limitations, Automated Pain Recognition (APR) powered by Artificial Intelligence (AI) offers a promising advancement. Notably, computer vision and machine learning have revolutionized our approach to identifying and addressing pain in non-verbal patients, including animals, with profound implications for both veterinary medicine and animal welfare. By leveraging the capabilities of AI algorithms, we can construct sophisticated models capable of analyzing diverse data inputs, encompassing not only facial expressions but also body language, vocalizations, and physiological signals, to provide precise and objective evaluations of an animal's pain levels. While the advancement of APR holds great promise for improving animal welfare by enabling better pain management, it also brings forth the need to overcome data limitations, ensure ethical practices, and develop robust ground truth measures. This narrative review aimed to provide a comprehensive overview, tracing the journey from the initial application of facial expression recognition for the development of pain scales in animals to the recent application, evolution, and limitations of APR, thereby contributing to understanding this rapidly evolving field.

    Keywords: Animal FACS, Computer Vision, Convolutional neural networks (CNNs), deep learning, facial expressions, machine learning, Pain recognition

    Received: 22 May 2024; Accepted: 03 Jul 2024.

    Copyright: © 2024 Chiavaccini, Gupta and Chiavaccini. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Ludovica Chiavaccini, University of Florida, Gainesville, United States

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.