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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/18796

Title: Augmenting Social Interactions: Realtime Behavioural Feedback using Social Signal Processing Techniques
Authors: Damian, Ionut
TAN, Chiew Seng Sean
Baur, Tobias
André, Elisabeth
Issue Date: 2015
Publisher: ACM
Citation: Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems CHI '15, p. 565-574
Abstract: Nonverbal 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.
URI: http://hdl.handle.net/1942/18796
DOI: 10.1145/2702123.2702314
ISI #: 000412395500067
ISBN: 9781450331456
Category: C1
Type: Proceedings Paper
Validation: vabb, 2018
Appears in Collections: Research publications

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