OkeyDoggy3D: A Mobile Application for Recognizing Stress-Related Behaviors in Companion Dogs Based on Three-Dimensional Pose Estimation through Deep Learning

Dogs often express their stress through physical motions that can be recognized by their owners. We propose a mobile application that analyzes companion dog's behavior and their three-dimensional poses via deep learning. As existing research on pose estimation has focused on humans, obtaining a large dataset comprising images showing animal joint locations is a challenge. Nevertheless, we generated such a dataset and used it to train an AI model. Furthermore, we analyzed circling behavior, which is associated with stress in companion dogs. To this end, we used the VideoPose3D model to estimate the 3D poses of companion dogs from the 2D pose estimation technique derived by the DeepLabCut model and developed a mobile app that provides analytical information on the stress-related behaviors, as well as the walking and isolation times, of companion dogs. Finally, we interviewed five certified experts to evaluate the validity and applicability of the app.

Automated summary

This study proposes a mobile application that utilizes deep learning to analyze the behavior and poses of companion dogs. By training an AI model and analyzing the 3D poses, the app provides analytical information on stress-related behaviors.