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Related Experiment Video

Updated: Oct 16, 2025

Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction

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Pain assessment in horses using automatic facial expression recognition through deep learning-based modeling.

Gabriel Carreira Lencioni¹, Rafael Vieira de Sousa², Edson José de Souza Sardinha²

¹Department of Preventive Veterinary Medicine and Animal Health of the School of Veterinary Medicine and Animal Science (FMVZ) of the University of São Paulo (USP), São Paulo, SP, Brazil.

|October 19, 2021

Summary

A new machine vision algorithm accurately assesses horse pain using facial expressions, offering real-time monitoring and aiding earlier diagnosis and treatment. This automated system supports the Horse Grimace Scale (HGS) for improved animal welfare.

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Area of Science:

Veterinary Medicine
Animal Welfare Science
Computer Vision

Background:

The Horse Grimace Scale (HGS) is a subjective pain assessment tool requiring trained observers.
Human observation limitations include availability, training consistency, and potential animal behavioral changes due to observer presence.
Objective, real-time pain assessment in horses is needed for timely veterinary intervention.

Purpose of the Study:

To develop and evaluate a machine vision algorithm for automated pain assessment in horses.
To create a computational classifier based on the Horse Grimace Scale (HGS) using machine learning.
To enable accurate, real-time pain monitoring in horses via video imaging.

Main Methods:

Collected video images of 7 horses undergoing castration over 6 days.
Utilized a machine vision algorithm trained with a Convolutional Neural Network (CNN).
Developed a pain facial image database through a labeling process for machine learning.

Main Results:

The CNN model achieved 75.8% accuracy in classifying three pain levels (none, moderate, obvious).
Accuracy increased to 88.3% when classifying pain as present or not present.
The algorithm demonstrated capability in automatically measuring pain through horses' facial expressions from video images.

Conclusions:

The developed machine vision algorithm shows promise for automatic pain assessment in horses.
This automated system can potentially improve pain diagnosis and treatment efficiency.
Further refinements are needed for routine clinical application, but the model is a significant step forward.