Machine learning-based affect detection within the context of human-horse interaction

Turke Althobaiti*, Stamos Katsigiannis, Daune West, Hassan Rabah, Naeem Ramzan

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

Abstract

This chapter focuses on the use of machine learning techniques within the field of affective computing, and more specifically for the task of emotion recognition within the context of human-horse interaction. Affective computing focuses on the detection and interpretation of human emotion, an application that could significantly benefit quantitative studies in the field of animal assisted therapy. The chapter offers a thorough description, an experimental design, and experimental results on the use of physiological signals, such as electroencephalography (EEG), electrocardiography (ECG), and electromyography (EMG) signals, for the creation and evaluation of machine learning models for the prediction of the emotional state of an individual during interaction with horses.
Original languageEnglish
Title of host publicationAI for Emerging Verticals
Subtitle of host publicationHuman-Robot Computing, Sensing and Networking
EditorsMuhammad Zeeshan Shakir, Naeem Ramzan
PublisherIET
Chapter3
ISBN (Electronic)9781785619830
ISBN (Print)9781785619823
DOIs
Publication statusPublished - 30 Nov 2020

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