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2014 | OriginalPaper | Buchkapitel

Multiresolution Feature Extraction During Psychophysiological Inference: Addressing Signals Asynchronicity

verfasst von : François Courtemanche, Aude Dufresne, Élise L. LeMoyne

Erschienen in: Physiological Computing Systems

Verlag: Springer Berlin Heidelberg

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Abstract

Predicting the psychological state of the user using physiological measures is one of the main objectives of physiological computing. While numerous works have addressed this task with great success, a large number of challenges remain to be solved in order to develop recognition approaches that can precisely and reliably feed human-computer interaction systems. This chapter focuses on one of these challenges which is the temporal asynchrony between different physiological signals within one recognition model. The chapter proposes a flexible and suitable method for feature extraction based on empirical optimisation of windows’ latency and duration. The approach is described within the theoretical framework of the psychophysiological inference and its common implementation using machine learning. The method has been experimentally validated (46 subjects) and results are presented. Empirically optimised values for the extraction windows are provided.

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Vorheriges Kapitel Extracting Emotions and Communication Styles from Prosody

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Titel: Multiresolution Feature Extraction During Psychophysiological Inference: Addressing Signals Asynchronicity
verfasst von: François Courtemanche
Aude Dufresne
Élise L. LeMoyne
Verlag: Springer Berlin Heidelberg
Buch: Physiological Computing Systems
Print ISBN: 978-3-662-45685-9

Electronic ISBN: 978-3-662-45686-6

Copyright-Jahr: 2014
DOI: https://doi.org/10.1007/978-3-662-45686-6_3

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