Measuring Human-Animal Interaction with Smartwatches: An Initial Experiment

Stolz, Katharina; Heyder, Teresa; Gloor, Peter A.; Posegga, Oliver

doi:10.1007/978-3-030-17238-1_10

Cited by 1 publication

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“…The system does so in an autonomous way and with good results. This proves that we are capable of creating a system that can recognize the emotions of a non-human animal species that has the ability to produce facial expressions and that it might be possible to detect these emotions by other methods, such as measuring the animal's heart rate, its temperature, or recording the sounds that they produce and feeding all this data into a system similar to the one created here [41]. While the system works with reasonable accuracy, it is worth pointing out that it could be improved in many ways.…”

Section: Discussionmentioning

confidence: 73%

Emotion Recognition in Horses with Convolutional Neural Networks

Corujo

Kieson²,

Schloesser

et al. 2021

Future Internet

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Creating intelligent systems capable of recognizing emotions is a difficult task, especially when looking at emotions in animals. This paper describes the process of designing a “proof of concept” system to recognize emotions in horses. This system is formed by two elements, a detector and a model. The detector is a fast region-based convolutional neural network that detects horses in an image. The model is a convolutional neural network that predicts the emotions of those horses. These two elements were trained with multiple images of horses until they achieved high accuracy in their tasks. In total, 400 images of horses were collected and labeled to train both the detector and the model while 40 were used to validate the system. Once the two components were validated, they were combined into a testable system that would detect equine emotions based on established behavioral ethograms indicating emotional affect through the head, neck, ear, muzzle, and eye position. The system showed an accuracy of 80% on the validation set, demonstrating that it is possible to predict emotions in animals using autonomous intelligent systems. Such a system has multiple applications including further studies in the growing field of animal emotions as well as in the veterinary field to determine the physical welfare of horses or other livestock.

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Section: Discussionmentioning

confidence: 73%