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

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

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Authors

Turke Althobaiti

Daune West

Hassan Rabah

Naeem Ramzan



Contributors

Muhammad Zeeshan Shakir
Editor

Naeem Ramzan
Editor

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.

Citation

Althobaiti, T., Katsigiannis, S., West, D., Rabah, H., & Ramzan, N. (2020). Machine learning-based affect detection within the context of human-horse interaction. In M. Z. Shakir, & N. Ramzan (Eds.), AI for Emerging Verticals; Human-robot computing, sensing and networking. IET

Online Publication Date Dec 15, 2020
Publication Date 2020
Deposit Date Dec 15, 2020
Publicly Available Date Mar 29, 2024
Publisher IET
Book Title AI for Emerging Verticals; Human-robot computing, sensing and networking.
Publisher URL https://shop.theiet.org/ai-for-emerging-verticals

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