Emotion recognition from physiological signals using wireless sensors for presence technologies

By Fatma Nasoz, Kaye Alvarez, Christine L. Lisetti, Neal Finkelstein. Cognition, Technology & Work, 2004

389 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Experimental and Cognitive Psychology, Human-Computer Interaction

View Paper: https://www.semanticscholar.org/paper/db3a4e9790cf585f332a7c98ffcabe1f64754a96

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

In Experimental and Cognitive Psychology

In Human-Computer Interaction

Citations in the corpus

ECG Pattern Analysis for Emotion Detection (2012, background): “The remainder of this section provides a brief overview of some of these methodologies, with the goal of demonstrating the way that ECG has been previously deployed.”
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.”
Affect Detection: An Interdisciplinary Review of Models, Methods, and Their Applications (2010, neutral): “emotions in their MAUI system [110].”
Human Emotion Recognition: Review of Sensors and Methods (2020, background): “Present a new approach how to analyze the physiological signals associated with emotions Sadness, amusement, fear, anger, surprise SKT, GSR BodyMedia, SenseWear armband [140]”
Emotion Recognition From EEG Using Higher Order Crossings (2010, methodology): “autonomous nervous system (galvanic skin response, heart rate, etc.) [15], [ 16 ].”
Emotion in Human-Computer Interaction (2012, methodology): “For different application scenarios, environmental conditions, and specific variant of emotional signs different classification methods have been used (King et al. 1995; Nasoz et al. 2004).”
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.”