Abstract

Emotions are feelings that are the result of biochemical processes in the body that are influenced by a variety of factors such as one's state of mind, situations, experiences, and surrounding environment. Emotions have an impact on one's ability to think and act. People interact with each other to share their thoughts and feelings. Emotions play a vital role in the field of medicine and can also strengthen the human computer interaction. There are different techniques being used to detect emotions based on facial features, texts, speech, and physiological signals. One of the physiological signal breathing is a parameter which represents an emotion. The rational belief that different breathing habits are correlated with different emotions has expanded the evidence for a connection between breathing and emotion. In this manuscript different recent investigations about the emotion recognition using respiration patterns have been reviewed. The aim of the survey is to sum up the latest technologies and techniques to help researchers develop a global solution for emotional detection system. Various researchers use benchmark datasets and few of them created their own dataset for emotion recognition. It is observed that many investigators used invasive sensors to acquire respiration signals that makes subject uncomfortable and conscious that affects the results. The numbers of subjects involved in the studies reviewed are of the same age and race which is the reason why the results obtained in those studies cannot be applied to diverse population. There is no single global solution exist.

Highlights

  • Emotions are an integral aspect of everyday life and are focused on conscious mental reaction to events, objects, and is linked to different physiological activities

  • The results show that the Auto-Mutual Information Function (AMIF) applied to the RR(t) classifies between relax and joy, joy, and each negative valence conditions and fear, sadness, and anger with an accuracy higher than 70% and area under the receiver operating characteristic curve index AUC >= 0.70

  • This paper reviewed methods implemented by several researchers in combination with technology implementation and interpretation to address the problem of emotion detection using respiration

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Summary

Introduction

Emotions are an integral aspect of everyday life and are focused on conscious mental reaction to events, objects, and is linked to different physiological activities. Children with autism spectrum disorder (ASD) typically find it difficult to understand, communicate and regulate feelings [2]. It can strengthen human-machine engagement by using emotional information in conversation [2]. Recognizing emotion with physical signals is not accurate and it is convenient for people to hide their true feelings to alter their voice or to manipulate their face [6]. These complications contribute to emotional recognition using physiological signals.

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