# Dynamics are Important for the Recognition of Equine Pain in Video

## TL;DR

This paper introduces a deep recurrent two-stream model for recognizing pain in horses from videos, emphasizing the importance of temporal information, and demonstrates it surpasses veterinary experts and existing methods.

## Contribution

It presents the first deep learning approach for equine pain recognition, highlighting the significance of temporal data and benchmarking against veterinary experts.

## Key findings

- Sequential models outperform single-frame models.
- The proposed method surpasses veterinary expert accuracy.
- Results outperform existing non-human pain detection methods.

## Abstract

A prerequisite to successfully alleviate pain in animals is to recognize it, which is a great challenge in non-verbal species. Furthermore, prey animals such as horses tend to hide their pain. In this study, we propose a deep recurrent two-stream architecture for the task of distinguishing pain from non-pain in videos of horses. Different models are evaluated on a unique dataset showing horses under controlled trials with moderate pain induction, which has been presented in earlier work. Sequential models are experimentally compared to single-frame models, showing the importance of the temporal dimension of the data, and are benchmarked against a veterinary expert classification of the data. We additionally perform baseline comparisons with generalized versions of state-of-the-art human pain recognition methods. While equine pain detection in machine learning is a novel field, our results surpass veterinary expert performance and outperform pain detection results reported for other larger non-human species.