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

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

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: Jan 13, 2021
• Publisher: IET
• Book Title: AI for Emerging Verticals; Human-robot computing, sensing and networking.
• Public URL: https://durham-repository.worktribe.com/output/1626485
• Publisher URL: https://shop.theiet.org/ai-for-emerging-verticals