A Review on Physiological Signal Based Emotion Detection

Annals of Emerging Technologies in Computing

article

Shahzad, Hina Fatima¹  Saleem, Adil Ali¹  Ahmed, Amna¹  Shehzadi, Kiran¹  Siddiqui, Hafeez Ur Rehman¹ 

[1] Khwaja Fareed University of Engineering and Information Technology

关键词: Human emotions;    Machine learning;    Physiological signals;    Respiration pattern;    Signal processing;   

DOI  :  10.33166/AETiC.2021.03.003

学科分类：电子与电气工程

来源: International Association for Educators and Researchers (IAER)

【 摘 要 】

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.

【 授权许可】

CC BY