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Sensors (Basel, Switzerland)2025; 25(3); doi: 10.3390/s25030859

Identifying Novel Emotions and Wellbeing of Horses from Videos Through Unsupervised Learning.

Abstract: This research applies unsupervised learning on a large original dataset of horses in the wild to identify previously unidentified horse emotions. We construct a novel, high-quality, diverse dataset of 3929 images consisting of five wild horse breeds worldwide at different geographical locations. We base our analysis on the seven Panksepp emotions of mammals "Exploring", "Sadness", "Playing", "Rage", "Fear", "Affectionate" and "Lust", along with one additional emotion "Pain" which has been shown to be highly relevant for horses. We apply the contrastive learning framework MoCo (Momentum Contrast for Unsupervised Visual Representation Learning) on our dataset to predict the seven Panksepp emotions and "Pain" using unsupervised learning. We significantly modify the MoCo framework, building a custom downstream classifier network that connects with a frozen CNN encoder that is pretrained using MoCo. Our method allows the encoder network to learn similarities and differences within image groups on its own without labels. The clusters thus formed are indicative of deeper nuances and complexities within a horse's mood, which can possibly hint towards the existence of novel and complex equine emotions.
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Publication Date: 2025-01-31 PubMed ID: 39943498PubMed Central: PMC11819734DOI: 10.3390/s25030859Google Scholar: Lookup
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  • Journal Article

Summary

This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.

This study utilizes machine learning to learn and identify new emotional states in horses from video footage. It employs a modified version of an established machine learning framework called Momentum Contrast (MoCo) to create a novel dataset and classifier for horse emotions.

Data Collection

The research team compiled a dataset of 3929 images depicting five breeds of wild horses from various global locations. The dataset’s size and variety are key to allowing the unsupervised learning process to capture a wide range of horse behaviours and emotions.

  • Unsupervised learning is a type of machine learning where a model learns to identify patterns without being explicitly told what to look for.
  • The dataset used in this study, created through fieldwork and video analysis, is large and diverse. It includes different horse breeds located in various geographical areas.

Emotional Framework

The analysis adopted the seven mammalian emotions suggested by neuroscientist Jaak Panksepp: Exploration, Sadness, Play, Rage, Fear, Affection, and Lust. An additional emotion was included – Pain – which is particularly relevant in analysing horse behaviour.

  • These emotions were not used as labels for the machine learning process but rather as a lens through which the outcomes could be understood.

Methodology

The Momentum Contrast (MoCo) machine learning framework was used to predict the identified emotions in an unsupervised manner. The MoCo framework was significantly modified to include a custom downstream classifier that connects with a pretrained CNN encoder.

  • The MoCo framework was trained using the collected dataset to understand and map out the visual representation of horse emotions.
  • The research introduces a new downstream classifier, which is a separate network designed to classify the output of the pretrained CNN encoder. The downstream classifier is not trained on any labelled data, enabling it to discover patterns independently.

Findings

The research methodology allowed the encoder network to autonomously learn the similarities and differences within image groups. The image clusters formed because of this learning process may indicate deeper complexity in a horse’s mood, potentially suggesting the existence of previously unidentified complex equine emotions.

  • The image clusters are not pre-defined categories, but groups discovered by the machine learning algorithm. The formation of these clusters indicates that horses could potentially express more nuanced and complex emotions than previously understood.

Cite This Article

APA
Bhave A, Kieson E, Hafner A, Gloor PA. (2025). Identifying Novel Emotions and Wellbeing of Horses from Videos Through Unsupervised Learning. Sensors (Basel), 25(3). https://doi.org/10.3390/s25030859

Publication

Sensors (Basel, Switzerland)
ISSN: 1424-8220
NlmUniqueID: 101204366
Country: Switzerland
Language: English
Volume: 25
Issue: 3

Researcher Affiliations

Bhave, Aarya
  • Massachusetts Institute of Technology, System Design & Management, Cambridge, MA 02142, USA.
Kieson, Emily
  • Equine International, Cambridge CB22 5LD, UK.
Hafner, Alina
  • TUM School of Computation, Information and Technology, Technical University of Munich, Arcisstraße 21, 80333 Munich, Germany.
Gloor, Peter A
  • Massachusetts Institute of Technology, System Design & Management, Cambridge, MA 02142, USA.

MeSH Terms

  • Animals
  • Horses / physiology
  • Emotions / physiology
  • Unsupervised Machine Learning
  • Video Recording

Conflict of Interest Statement

Author Emily Kieson serves on the board of Equine International. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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