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Multimodal Emotion Classification Using Physiological Signals and Machine Learning Techniques
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Author(s): Adhitya Velip (Goa College of Engineering, India), Hassanali G. Virani (Goa College of Engineering, India)and Amita Umesh Dessai (Goa College of Engineering, India)
Copyright: 2027
Pages: 32
Source title:
Encyclopedia of Modern Artificial Intelligence
Source Author(s)/Editor(s): Mehdi Khosrow-Pour, D.B.A. (Founding Editor-in-Chief, Information Resources Management Journal (IRMJ), USA)
DOI: 10.4018/407569
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Abstract
Emotions play a vital role in shaping our behavior and decisions, influencing our physiological and mental state. Affective computing focuses on developing computer systems to understand and simulate human emotions. The method of emotion classification involves thorough collection, preprocessing, and modelling using advanced algorithms such as machine learning and deep learning. The review covers various techniques for emotion elicitation, self-assessment, preprocessing, and unimodal and multimodal classification, along with the utilization of physiological signals. The study examines openly available databases, emotion labels, feature extraction, feature selection, and feature reduction techniques used in emotion classification. This article focuses on physiological signals collected from wearable devices with sensors, including blood volume pulse, skin temperature, optomyography, and galvanic skin response. The goal is to highlight the latest advancements and identify opportunities for innovative machine learning, deep learning, and fusion techniques in classifying emotions.
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