Emotion recognition system using short-term monitoring of physiological signals

By Kyung Hwan Kim, S. Bang, Si-Hong Kim. Medical and Biological Engineering and Computing, 2004

959 citations recorded by the source corpus.

Subfields: Cardiology and Cardiovascular Medicine, Cognitive Neuroscience, Experimental and Cognitive Psychology

View Paper: https://www.semanticscholar.org/paper/eeec377019b2e81f805d1032e350f4b5e5ed3864

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In Cardiology and Cardiovascular Medicine

In Cognitive Neuroscience

In Experimental and Cognitive Psychology

Citations in the corpus

Emotion recognition based on physiological changes in music listening (2008, background): “…of consistency of ANS specificity in the literature comes as no surprise since those influencing factors (contexts) vary widely across emotion studies and that, therefore, consistent ANS specificity among emotions could only be found if the compound of emotion-plus-context pattern is decomposed.”
Recognising realistic emotions and affect in speech: State of the art and lessons learnt from the first challenge (2011, background): “…dealing with analysis of biosignals, such as heart rate, skin conductivity, or brain-waves are found (e.g., de Gelder et al., 1999; de Gelder and Vroomen, 2000; Picard et al., 2001; Kim et al., 2004, 2005; Nasoz et al., 2004; Takahashi, 2004; Wagner et al., 2005), also in combination with speech.”
Classification of human emotion from EEG using discrete wavelet transform (2010, background): “Several approaches have been reported by different researchers on finding the correlation between the emotional changes and EEG signals [6-8].”
Review on Psychological Stress Detection Using Biosignals (2019, neutral): “(2004) [231] 50 subjects 7-8 Videos, images, sounds EDA, SKT, ECG SVM 78.”
Toward Emotion Recognition in Car-Racing Drivers: A Biosignal Processing Approach (2008, methodology): “The latest findings indicate that emotional state is reflected in human biosignals; thus, emotion recognition methodologies based on the classification of extracted series of features from these biosignals are constantly gaining popularity [21]–[24].”
An empirical study of machine learning techniques for affect recognition in human–robot interaction (2005, background): “Support Vector Machines based emotion-recognizer has also been investigated in [13], where correct classification accuracies of 78.4% and 61.8%, 41.7% were reported for the reorganization of three, four and five emotions categories respectively.”
Human emotion recognition from EEG-based brain–computer interface using machine learning: a comprehensive review (2022, background): “The developed classiﬁer has an important inﬂuence on emotion recognition accuracy [131].”
Human Emotion Recognition: Review of Sensors and Methods (2020, background): “8%, for the recognition of three and four categories, respectively [133]”
Emotion recognition using brain activity (2008, methodology): “Another method of measuring human emotion, is using physiological signals [24, 34].”