Paper Info
Title
Towards Automatic and Unobtrusive Recognition of Primary-Process Emotions in Body Postures
Abstract
Recent years demonstrate an increased research in automatic recognition of emotions in whole-body gestures. However, most of them rely on emotional models that are still being contested or require an obtrusive way of collecting the data. We study primitive postures based on 7 primary-process and clinically measured emotions. We portray postures from theatre in front of the motion capture sensor and we conduct online surveys to discriminate primary-process emotions. We analyze low-level features from postural joints data and reveal RAGE patterns which we will use in future real-time affective interactions.
Year
DOI
Venue
2013
10.1109/ACII.2013.121
Affective Computing and Intelligent Interaction
Keywords
Field
DocType
behavioural sciences computing,emotion recognition,image motion analysis,real-time systems,RAGE patterns,automatic emotion recognition,automatic recognition,body postures,clinically measured emotions,emotional models,low-level features,motion capture sensor,online surveys,postural joints data,primary-process emotions,primitive posture,real-time affective interaction,unobtrusive recognition,whole-body gestures,Affective Computing,Affective Neuroscience,Body Posture Analysis,Human-Computer Interaction,Primary-Process Emotions,Primitive Postures
Social psychology,Motion capture,Communication,Gesture,Emotion recognition,Psychology,Affective computing,Affect (psychology),Affective neuroscience
Conference
ISSN
Citations 
PageRank 
2156-8103
1
0.34
References 
Authors
13
2
Name
Order
Citations
PageRank
Marko Radeta121.43
Marco Maiocchi210.34