During remote video-mediated assistance, instructors often guide workers through problems and instruct them to perform unfamiliar or complex operations. However, the workers' performance might deteriorate due to stress. We argue that informing biofeedback to the instructor, can improve communication and lead to lower stress. This paper presents a thorough investigation on mental workload and stress perceived by twenty participants, paired up in an instructor-worker scenario, performing remote video-mediated tasks. The interface conditions differ in task, facial and biofeedback communication. Two self-report measures are used to assess mental workload and stress. Results show that pairs reported lower mental workload and stress when instructors are using the biofeedback as compared to using interfaces with facial view. Significant correlations were found on task performance with reducing stress (i.e. increased task engagement and decreased worry) for instructors and declining mental workload (i.e. increased performance) for workers. Our findings provide insights to advance video-mediated interfaces for remote collaborative work.

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.

Figure 1. Concept of the Logue system: A user wearing an HMD while giving a public speech (left). Using various sensors and social signal processing techniques (not illustrated), the user receives realtime feedback on his own behaviour superimposed on field of view (right). 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.