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.

Chapter Contents:

• 3.1 Introduction
• 3.2 Background
• 3.3 Experimental protocol
• 3.3.1 Field experiment setting
• 3.3.2 Experimental data acquisition
• 3.3.3 Self-reporting of emotional state
• 3.3.4 Participants
• 3.4 Analysis of captured data
• 3.4.1 Pre-processing of physiological signals
• 3.4.2 Extraction of features from physiological signals
• 3.4.3 Emotion labels
• 3.5 Experimental results
• 3.6 Discussion
• 3.7 Conclusion
• References

Inspec keywords: learning (artificial intelligence); user interfaces; affective computing

Other keywords: human-horse interaction; machine-learning techniques; human emotion; experimental design; physiological signals; affective computing; animal-assisted therapy

Subjects: User interfaces; Knowledge engineering techniques