We use cookies to ensure that we give you the best experience on our website. By continuing to browse this repository, you give consent for essential cookies to be used. You can read more about our Privacy and Cookie Policy.

Durham Research Online
You are in:

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

Althobaiti, Turke and Katsigiannis, Stamos and West, Daune and Rabah, Hassan and Ramzan, Naeem (2020) 'Machine learning-based affect detection within the context of human-horse interaction.', in AI for Emerging Verticals; Human-robot computing, sensing and networking. .


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.

Item Type:Book chapter
Full text:(AM) Accepted Manuscript
Download PDF
Publisher Web site:
Date accepted:No date available
Date deposited:13 January 2021
Date of first online publication:15 December 2020
Date first made open access:13 January 2021

Save or Share this output

Look up in GoogleScholar