Emotion Recognition Using Bio-sensors: First Steps towards an Automatic System

By Andreas Haag, Silke Goronzy, Peter Schaich, Jason B. Williams. Affective Dialogue Systems, 2004

535 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Electrical and Electronic Engineering, Experimental and Cognitive Psychology

View Paper: https://www.semanticscholar.org/paper/8a323d039c02085ca250cde4dba964c6ab6be74d

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In Cognitive Neuroscience

In Electrical and Electronic Engineering

In Experimental and Cognitive Psychology

Citations in the corpus

Emotion in Human-Computer Interaction (2012, background): “Fuzzy logic approaches [24] and neural nets [25], [26] are examples for this.”
Computational models of emotion (2010, methodology): “Finally, we illustrate how this component model view can help guide work in evaluating and contrasting alternative computational models of emotion.”
From Physiological Signals to Emotions: Implementing and Comparing Selected Methods for Feature Extraction and Classification (2005, methodology): “Although many efforts have been taken recently to recognize emotions using facial expressions, speech and physiological signals [1, 2, 3, 4 , 5], current recognition systems are not yet advanced enough to be used in realistic applications.”
EEG-based classification of positive and negative affective states (2014, background): “[4,33-35] Although normative ratings have been established for both the IAPS and IADS, studies continue to gather and utilize subjective responses for each individual in order to take into account individual differences.”
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.”
Fundamentals of physiological computing (2009, background): “The latter has been used by Haag et al. (2004) to induce changes in valence as well as activation associated with emotion.”
EEG-Based Emotion Recognition: A State-of-the-Art Review of Current Trends and Opportunities (2020, methodology): “…tree, Bayesian networks, support vector machines (SVM) [133, 135], canonical correlation analysis (CCA) [136], artiﬁcial neural network (ANN) [137], linear discriminant analysis (LDA) [138], and Marquardt back-propagation (MBP) [139] were used by researchers to classify the diﬀerent emotions.”
ECG Pattern Analysis for Emotion Detection (2012, background): “From a more clinical perspective, Sinha et al. [7], looked into the possibility of differentiating emotions through cardiovascular measurements.”
Categorical and dimensional affect analysis in continuous input: Current trends and future directions (2013, background): “activities), that is correlated with negatively valenced emotions [42], heart rate that increases with negatively valenced emotions such as fear, heart rate variability that indicates a state of relaxation or mental stress, and respiration rate (how deep and fast the breath is) that becomes irregula”
Emotion recognition using brain activity (2008, methodology): “[24] explore the use of bio-sensors, to find out whether they are suitable for complementing or replacing other modalities in measuring emotion.”
Human Emotion Recognition: Review of Sensors and Methods (2020, background): “A low HRV indicates a state of relaxation, whereas an increased HRV indicates a potential state of mental stress or frustration [100].”
Emotion representation, analysis and synthesis in continuous space: A survey (2011, background): “and fast the breath is) that becomes irregular with more aroused emotions like anger or fear [16], [17].”