DocumentCode
2872529
Title
How to Do Multimodal Detection of Affective States?
Author
Gonzalez-Sanchez, Javier ; Christopherson, Robert M. ; Chavez-Echeagaray, Maria Elena ; Gibson, David C. ; Atkinson, Robert ; Burleson, Winslow
Author_Institution
Sch. of Comput., Arizona State Univ., Tempe, AZ, USA
fYear
2011
fDate
6-8 July 2011
Firstpage
654
Lastpage
655
Abstract
The human-element is crucial for designing and implementing interactive intelligent systems, and therefore on instructional design. This tutorial provides a description and hands-on demonstration for detection of affective states and a description of devices, methodologies and tools necessary for automatic detection of affective states. Automatic detection of affective states requires that the computer sense information that is complex and diverse, it can range from brain-waves signals, and biofeedback readings to face-based and gesture emotion recognition to posture and pressure sensing. Obtaining, processing and understanding that information, to create systems that improve learning, requires the use of several sensing devices (and their perceiving algorithms) and the application of software tools.
Keywords
emotion recognition; face recognition; image sensors; intelligent tutoring systems; interactive systems; affective state detection; automatic detection; biofeedback readings; brain wave signal; computer sense information; face based emotion recognition; gesture emotion recognition; instructional design; interactive intelligent system; multimodal detection; sensing devices; software tools; Artificial intelligence; Emotion recognition; Humans; Presses; Sensors; Software; Tutorials; affective state; empathetic systems; multimodal; physiological activity; sensors; student affect inference;
fLanguage
English
Publisher
ieee
Conference_Titel
Advanced Learning Technologies (ICALT), 2011 11th IEEE International Conference on
Conference_Location
Athens, GA
ISSN
2161-3761
Print_ISBN
978-1-61284-209-7
Electronic_ISBN
2161-3761
Type
conf
DOI
10.1109/ICALT.2011.206
Filename
5992431
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