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Emotion recognition from physiological signals using wireless sensors for presence technologies

Published: 01 February 2004 Publication History

Abstract

In this article we describe a new approach to enhance presence technologies. First, we discuss the strong relationship between cognitive processes and emotions and how human physiology is uniquely affected when experiencing each emotion. Secondly, we introduce our prototype multimodal affective user interface. In the remainder of the paper we describe the emotion elicitation experiment we designed and conducted and the algorithms we implemented to analyse the physiological signals associated with emotions. These algorithms can then be used to recognise the affective states of users from physiological data collected via non-invasive technologies. The affective intelligent user interfaces we plan to create will adapt to user affect dynamically in the current context, thus providing enhanced social presence.

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  • (2024)A Survey of Cutting-edge Multimodal Sentiment AnalysisACM Computing Surveys10.1145/365214956:9(1-38)Online publication date: 25-Apr-2024
  • (2024)Federated learning inspired privacy sensitive emotion recognition based on multi-modal physiological sensorsCluster Computing10.1007/s10586-023-04133-427:3(3179-3201)Online publication date: 1-Jun-2024
  • (2024)Multimodal emotion classification using machine learning in immersive and non-immersive virtual realityVirtual Reality10.1007/s10055-024-00989-y28:2Online publication date: 6-May-2024
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Published In

cover image Cognition, Technology and Work
Cognition, Technology and Work  Volume 6, Issue 1
February 2004
62 pages
ISSN:1435-5558
EISSN:1435-5566
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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 February 2004

Author Tags

  1. Emotion recognition
  2. Social presence
  3. User interfaces

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Cited By

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  • (2024)A Survey of Cutting-edge Multimodal Sentiment AnalysisACM Computing Surveys10.1145/365214956:9(1-38)Online publication date: 25-Apr-2024
  • (2024)Federated learning inspired privacy sensitive emotion recognition based on multi-modal physiological sensorsCluster Computing10.1007/s10586-023-04133-427:3(3179-3201)Online publication date: 1-Jun-2024
  • (2024)Multimodal emotion classification using machine learning in immersive and non-immersive virtual realityVirtual Reality10.1007/s10055-024-00989-y28:2Online publication date: 6-May-2024
  • (2023)EEG-Based Subject-Independent Emotion Recognition Using Gated Recurrent Unit and Minimum Class ConfusionIEEE Transactions on Affective Computing10.1109/TAFFC.2022.317971714:4(2740-2750)Online publication date: 1-Oct-2023
  • (2022)Applied Affective ComputingundefinedOnline publication date: 25-Jan-2022
  • (2021)Detecting Lies: Finding the Degree of Falsehood from Observers’ Physiological Responses2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC)10.1109/SMC52423.2021.9659279(1959-1965)Online publication date: 17-Oct-2021
  • (2021)Nonlinear classification of emotion from EEG signal based on maximized mutual informationExpert Systems with Applications: An International Journal10.1016/j.eswa.2021.115605185:COnline publication date: 15-Dec-2021
  • (2021)Investigating 4D movie audiences’ emotional responses to motion effects and empathyComputers in Human Behavior10.1016/j.chb.2021.106797121:COnline publication date: 1-Aug-2021
  • (2021)Music mood and human emotion recognition based on physiological signals: a systematic reviewMultimedia Systems10.1007/s00530-021-00786-628:1(21-44)Online publication date: 22-Apr-2021
  • (2020)EEG-Based Emotion RecognitionComputational Intelligence and Neuroscience10.1155/2020/88754262020Online publication date: 1-Jan-2020
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