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|
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|Publisher Web site:||https://shop.theiet.org/ai-for-emerging-verticals|
|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|
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