Augmenting Social Interactions: Realtime Behavioural Feedback using Social Signal Processing Techniques
Ionut Damian, Chiew Seng Sean Tan, Tobias Baur, Johannes Schöning, Kris Luyten, Elisabeth Andre
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.
ACM DL: http://dl.acm.org/citation.cfm?id=2702314