Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/18796
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dc.contributor.authorDamian, Ionut-
dc.contributor.authorTAN, Chiew Seng Sean-
dc.contributor.authorBaur, Tobias-
dc.contributor.authorSCHOENING, Johannes-
dc.contributor.authorLUYTEN, Kris-
dc.contributor.authorAndré, Elisabeth-
dc.date.accessioned2015-04-27T13:54:34Z-
dc.date.available2015-04-27T13:54:34Z-
dc.date.issued2015-
dc.identifier.citationProceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems CHI '15, p. 565-574-
dc.identifier.isbn9781450331456-
dc.identifier.urihttp://hdl.handle.net/1942/18796-
dc.description.abstractNonverbal and unconscious behaviour is an important component of daily human-human interaction. This is especially true in situations such as public speaking, job interviews or information sensitive conversations, where researchers have shown that an increased awareness of one’s behaviour can improve the outcome of the interaction. With wearable technology, such as Google Glass, we now have the opportunity to augment social interactions and provide realtime feedback on one’s behaviour in an unobtrusive way. In this paper we present Logue, a system that provides realtime feedback on the presenters’ openness, body energy and speech rate during public speaking. The system analyses the user’s nonverbal behaviour using social signal processing techniques and gives visual feedback on a head-mounted display. We conducted two user studies with a staged and a real presentation scenario which yielded that Logue’s feedback was perceived helpful and had a positive impact on the speaker’s performance.-
dc.language.isoen-
dc.publisherACM-
dc.rightsCopyright is held by the owner/author(s). Publication rights licensed to ACM.Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Permissions@acm.org.-
dc.subject.othercomputer-enhanced interaction; behaviour analysis; peripheral feedback; social signal processing-
dc.titleAugmenting Social Interactions: Realtime Behavioural Feedback using Social Signal Processing Techniques-
dc.typeProceedings Paper-
local.bibliographicCitation.conferencedateApril 18-23, 2015-
local.bibliographicCitation.conferencenamethe 33rd Annual ACM Conference on Human Factors in Computing Systems CHI '15-
local.bibliographicCitation.conferenceplaceSeoul, South-Korea-
dc.identifier.epage574-
dc.identifier.spage565-
local.bibliographicCitation.jcatC1-
local.publisher.placeNew York, NY, USA-
local.type.refereedRefereed-
local.type.specifiedProceedings Paper-
local.identifier.vabbc:vabb:394572-
dc.identifier.doi10.1145/2702123.2702314-
dc.identifier.isi000412395500067-
local.bibliographicCitation.btitleProceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems CHI '15-
item.fullcitationDamian, Ionut; TAN, Chiew Seng Sean; Baur, Tobias; SCHOENING, Johannes; LUYTEN, Kris & André, Elisabeth (2015) Augmenting Social Interactions: Realtime Behavioural Feedback using Social Signal Processing Techniques. In: Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems CHI '15, p. 565-574.-
item.contributorDamian, Ionut-
item.contributorTAN, Chiew Seng Sean-
item.contributorBaur, Tobias-
item.contributorSCHOENING, Johannes-
item.contributorLUYTEN, Kris-
item.contributorAndré, Elisabeth-
item.validationecoom 2019-
item.validationvabb 2018-
item.accessRightsOpen Access-
item.fulltextWith Fulltext-
Appears in Collections:Research publications
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