Using activity-related behavioural features towards more effective automatic stress detection.

This paper introduces activity-related behavioural features that can be automatically extracted from a computer system, with the aim to increase the effectiveness of automatic stress detection. The proposed features are based on processing of appropriate video and accelerometer recordings taken from...

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Autores principales:	Dimitris Giakoumis, Anastasios Drosou, Pietro Cipresso, Dimitrios Tzovaras, George Hassapis, Andrea Gaggioli, Giuseppe Riva
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2012
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/785796857bed47928e136664410ff696
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id	oai:doaj.org-article:785796857bed47928e136664410ff696
record_format	dspace
spelling	oai:doaj.org-article:785796857bed47928e136664410ff6962021-11-18T07:05:02ZUsing activity-related behavioural features towards more effective automatic stress detection.1932-620310.1371/journal.pone.0043571https://doaj.org/article/785796857bed47928e136664410ff6962012-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23028461/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203This paper introduces activity-related behavioural features that can be automatically extracted from a computer system, with the aim to increase the effectiveness of automatic stress detection. The proposed features are based on processing of appropriate video and accelerometer recordings taken from the monitored subjects. For the purposes of the present study, an experiment was conducted that utilized a stress-induction protocol based on the stroop colour word test. Video, accelerometer and biosignal (Electrocardiogram and Galvanic Skin Response) recordings were collected from nineteen participants. Then, an explorative study was conducted by following a methodology mainly based on spatiotemporal descriptors (Motion History Images) that are extracted from video sequences. A large set of activity-related behavioural features, potentially useful for automatic stress detection, were proposed and examined. Experimental evaluation showed that several of these behavioural features significantly correlate to self-reported stress. Moreover, it was found that the use of the proposed features can significantly enhance the performance of typical automatic stress detection systems, commonly based on biosignal processing.Dimitris GiakoumisAnastasios DrosouPietro CipressoDimitrios TzovarasGeorge HassapisAndrea GaggioliGiuseppe RivaPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 7, Iss 9, p e43571 (2012)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Dimitris Giakoumis Anastasios Drosou Pietro Cipresso Dimitrios Tzovaras George Hassapis Andrea Gaggioli Giuseppe Riva Using activity-related behavioural features towards more effective automatic stress detection.
description	This paper introduces activity-related behavioural features that can be automatically extracted from a computer system, with the aim to increase the effectiveness of automatic stress detection. The proposed features are based on processing of appropriate video and accelerometer recordings taken from the monitored subjects. For the purposes of the present study, an experiment was conducted that utilized a stress-induction protocol based on the stroop colour word test. Video, accelerometer and biosignal (Electrocardiogram and Galvanic Skin Response) recordings were collected from nineteen participants. Then, an explorative study was conducted by following a methodology mainly based on spatiotemporal descriptors (Motion History Images) that are extracted from video sequences. A large set of activity-related behavioural features, potentially useful for automatic stress detection, were proposed and examined. Experimental evaluation showed that several of these behavioural features significantly correlate to self-reported stress. Moreover, it was found that the use of the proposed features can significantly enhance the performance of typical automatic stress detection systems, commonly based on biosignal processing.
format	article
author	Dimitris Giakoumis Anastasios Drosou Pietro Cipresso Dimitrios Tzovaras George Hassapis Andrea Gaggioli Giuseppe Riva
author_facet	Dimitris Giakoumis Anastasios Drosou Pietro Cipresso Dimitrios Tzovaras George Hassapis Andrea Gaggioli Giuseppe Riva
author_sort	Dimitris Giakoumis
title	Using activity-related behavioural features towards more effective automatic stress detection.
title_short	Using activity-related behavioural features towards more effective automatic stress detection.
title_full	Using activity-related behavioural features towards more effective automatic stress detection.
title_fullStr	Using activity-related behavioural features towards more effective automatic stress detection.
title_full_unstemmed	Using activity-related behavioural features towards more effective automatic stress detection.
title_sort	using activity-related behavioural features towards more effective automatic stress detection.
publisher	Public Library of Science (PLoS)
publishDate	2012
url	https://doaj.org/article/785796857bed47928e136664410ff696
work_keys_str_mv	AT dimitrisgiakoumis usingactivityrelatedbehaviouralfeaturestowardsmoreeffectiveautomaticstressdetection AT anastasiosdrosou usingactivityrelatedbehaviouralfeaturestowardsmoreeffectiveautomaticstressdetection AT pietrocipresso usingactivityrelatedbehaviouralfeaturestowardsmoreeffectiveautomaticstressdetection AT dimitriostzovaras usingactivityrelatedbehaviouralfeaturestowardsmoreeffectiveautomaticstressdetection AT georgehassapis usingactivityrelatedbehaviouralfeaturestowardsmoreeffectiveautomaticstressdetection AT andreagaggioli usingactivityrelatedbehaviouralfeaturestowardsmoreeffectiveautomaticstressdetection AT giusepperiva usingactivityrelatedbehaviouralfeaturestowardsmoreeffectiveautomaticstressdetection
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Using activity-related behavioural features towards more effective automatic stress detection.

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