Paper Info
Title
Feature extraction for psychophysiological load assessment in unconstrained scenarios.
Abstract
The relevance of psychophysiological measurements for affective computing and emotion analysis applications has been widely recognized. However, and although several authors have studied the informative content of parameters derived from cardiovascular and other modalities, feature extraction remains an open topic in the field. This is particularly relevant in scenarios where the autonomic nervous system triggering stimuli are unknown. In this paper, we analyze a set of features extracted from multimodal biosignal data, applicable to the assessment of psychophysiological load in unconstrained settings. Experimental evaluation is performed on real world data, collected both from control subjects and subjects with a strong clinical background, in a context of questionnaire-based clinical history reporting. The devised feature set has shown promising properties, making it prone to complement the more traditional measurements.
Year
DOI
Venue
2012
10.1109/EMBC.2012.6347037
EMBC
Keywords
Field
DocType
stimulus triggering,psychophysiological measurements,multimodal biosignal data,neurophysiology,biomedical measurement,questionnaire based clinical history reporting,autonomic nervous system,medical signal processing,emotion analysis,psychology,feature extraction,unconstrained scenarios,psychophysiological load assessment,affective computing
Modalities,Computer vision,Neurophysiology,Computer science,Feature extraction,Speech recognition,Feature set,Artificial intelligence,Affective computing,Biosignal,Machine learning
Conference
Volume
ISSN
ISBN
2012
1557-170X
978-1-4577-1787-1
Citations 
PageRank 
References 
3
0.46
2
Authors
5
Name
Order
Citations
PageRank
Hugo Silva122730.18
Ana L. N. Fred21317195.30
Susana Eusebio330.46
Marco Torrado430.46
Silvia Ouakinin530.46