Affect Detection for Human-Horse Interaction

In this work, we aim to study the potential use of affect recognition techniques for examining the interaction between humans and horses using qualitative and quantitative methods. To this end, we propose a multi-modal portable system for physiological signal acquisition such as the electrocardiogram (ECG), electromyogram (EMG), and electroencephalogram (EEG). The proposed system is used to acquire signals while users are interacting with horses. The captured signals will then be used in order to quantitatively evaluate human and equine interaction by mapping the signals to the emotional state of the subjects using machine learning techniques. In this preliminary study, ECG based features were utilised in order to create a supervised classification model that can identify emotions elicited during human-horse interaction. Experimental results provide evidence about the efficiency of the proposed approach in distinguishing between negative and positive emotions, reaching a classification accuracy of 74.21%.