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Human Emotion Recognition using Physiological Signals: A Survey

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Document pages: 9 pages

Abstract: Human emotions are one of the ways to express our feelings. Affective Computing originates from the study of human emotions. Over the years, psychologists have developed various emotional models to explain the emotional or affective states of humans. Affective Computing uses various models of emotion and machine learning algorithms to classify emotions. Machine Learning enables computers to learn from the training datasets and classify new input, thus it can be effectively used to teach computers to understand human emotions. This paper focuses on a survey of human emotion recognition using physiological signals related to the human body like Electrocardiogram (ECG), Electroencephalogram (EEG), Electromyogram (EMG), Galvanic Skin Response (GSR), Respiration (RSP), Skin Temperature (SKT), etc. and also their advantages and disadvantages. It also describes challenges in physiological sensing for Affective Computing.

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