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.
|Title of host publication||AI for Emerging Verticals|
|Subtitle of host publication||Human-Robot Computing, Sensing and Networking|
|Editors||Muhammad Zeeshan Shakir, Naeem Ramzan|
|Publication status||Published - 30 Nov 2020|