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 SCHOENING, Johannes LUYTEN, Kris André, Elisabeth |
Issue Date: | 2015 | Publisher: | ACM | Source: | 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. | Keywords: | computer-enhanced interaction; behaviour analysis; peripheral feedback; social signal processing | Document URI: | http://hdl.handle.net/1942/18796 | ISBN: | 9781450331456 | DOI: | 10.1145/2702123.2702314 | ISI #: | 000412395500067 | Rights: | Copyright 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. | Category: | C1 | Type: | Proceedings Paper | Validations: | ecoom 2019 vabb 2018 |
Appears in Collections: | Research publications |
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p565-damian.pdf | Published version | 2.85 MB | Adobe PDF | View/Open |
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