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Empathic communication allows individuals to perceive and understand the feeling and emotion of the person with whom they are interacting. This could be particularly important during remote collaboration (such as remote assistance or distance learning) to enhance the social and emotional understanding of geographically distributed partners. However, supporting awareness in remote collaboration is very challenging especially when the interaction with the remote parties results in less information that can be communicated than in a physical interaction. We explore the effect of visualization using physiological cues that allow users to interpret emotional behaviors of remote parties with whom they are interacting in real time. The proposed visual representation allows users to infer emotional patterns from physiological cues that can potentially influence their communication approach toward a more aggressive style or maintain passive and peaceful interaction. We conducted a study involving participants who were paired up for a collaborative assessment task, interacting via voice only, videoconference, or a visual representation of the physiological measurements. Participants perceived the usage of our visual representation with higher group cohesiveness than using voice-only interaction. Further analysis shows that the visual representation significantly increases the positive affect score (i.e., participants are perceived to be more alert and demonstrate less distress) during remote collaboration. We discuss the possibilities of the proposed visual representation to support empathic communication during remote collaboration, and the benefits to the remote partners of having positive affect and group cohesiveness.

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.

Non-verbal and unconscious behavior plays an important role for efficient human-to-human communication but are often undervalued when training people to become better communicators. This is particularly true for public speakers who need not only behave according to a social etiquette but do so while generating enthusiasm and interest for dozens if not hundreds of other persons. In this paper we propose the concept of social augmentation using wearable computing with the goal of giving users the ability to continuously monitor their performance as a communicator. To this end we explore interaction modalities and feedback mechanisms which would lend themselves to this task.

Intelligent User Interfaces can benefit from having knowledge on the user's emotion. However, current implementations to detect affective states, are often constraining the user's freedom of movement by instrumenting her with sensors. This prevents affective computing from being deployed in naturalistic and ubiquitous computing contexts. In this paper, we present a novel system called mASqUE, which uses a set of association rules to infer someone's affective state from their body postures. This is done without any user instrumentation and using off-the-shelf and non-expensive commodity hardware: a depth camera tracks the body posture of the users and their postures are also used as an indicator of their openness. By combining the posture information with physiological sensors measurements we were able to mine a set of association rules relating postures to affective states. We demonstrate the possibility of inferring affective states from body postures in ubiquitous computing environments and our study also provides insights how this opens up new possibilities for IUI to access the affective states of users from body postures in a nonintrusive way.