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. in- creased performance) for workers. Our findings provide insights to advance video-mediated interfaces for remote collaborative work.

Storyboarding is a technique that is often used for the conception of new interactive systems. A storyboard illustrates graphically how a system is used by its users and what a typical context of usage is. Although the informal notation of a storyboard stimulates creativity, and makes them easy to understand for everyone, it is more difficult to integrate in further steps in the engineering process. We present an approach, "Time In Storyboards" (Timisto), to extract valuable information on how various interactions with the system are positioned in time with respect to each other. Timisto does not interfere with the creative process of storyboarding, but maximizes the structured information about time that can be deduced from a storyboard.

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.

The television remains a central hub in the home environment. We believe that in order to maintain its central role future TV's will need to incorporate empathic features. These will be delivered by interacting with other personal devices in home and services in the cloud. This position paper illustrates the common as well as the individual views of several Belgian partners working around a common scenario in the ITEA2 'Empathic Products' project.

In todays videogames user feedback is often provided through raw statistics and scoreboards. We envision that incorporating empathic feedback matching the player's current mood will improve the overall gaming experience. In this paper we present Bro-cam, a novel system that provides empathic feedback to the player based on their body postures. Different body postures of the players are used as an indicator for their openness. From their level of openness, Bro-cam profiles the players into different personality types ranging from introvert to extrovert. Empathic feedback is then automatically generated and matched to their preferences for certain humoristic feedback statements. We use a depth camera to track the player's body postures and movements during the game and analyze these to provide customized feedback. We conducted a user study involving 32 players to investigate their subjective assessment on the empathic game feedback. Semi-structured interviews reveal that participants were positive about the empathic feedback and Bro-cam significantly improves their game experience.