Delays in direct teleoperation decouple operator input from robot feedback. We frame this not as a unitary problem but as three facets of operator uncertainty: (1) communication, when commands take effect, (2) trajectory, how inputs map to motion, and (3) environmental, how external factors alter outcomes. We externalized each facet through predictive visualizations: Network, Path, and Envelope. In a controlled study with 24 participants (novices in telerobotics) navigating a simulated robot under a fixed 2.56s round-trip delay, we compared these visualizations against a delayed-video baseline. Path significantly shortened task time, lowered perceived cognitive load, and reduced reliance on reactive "move-and-wait" behavior. Envelope lowered cognitive load but did not significantly reduce reactive behavior or improve performance, while Network had no measurable effect. These results indicate that predictive support is effective only when trajectory uncertainty is externalized, enabling operators to move from reactive to more proactive control

Ubiquitous environments, such as smart homes, are becoming more intelligent and autonomous. As a result, their behavior becomes harder to grasp and unintended behavior becomes more likely. Researchers have contributed tools to better understand and validate an environments' past behavior (e.g. logs, end-user debugging), and to prevent unintended behavior. There is, however, a lack of tools that help users understand the future behavior of such an environment. Information about the actions it will perform, and why it will perform them, remains concealed. In this paper, we contribute FORTNIoT, a well-defined approach that combines self-sustaining predictions (e.g. weather forecasts) and simulations of trigger-condition-action rules to deduce when these rules will trigger in the future and what state changes they will cause to connected smart home entities. We implemented a proof-of-concept of this approach, as well as a visual demonstrator that shows such predictions, including causes and effects, in an overview of a smart home's behavior. A between-subject evaluation with 42 participants indicates that FORTNIoT predictions lead to a more accurate understanding of the future behavior, more confidence in that understanding, and more appropriate trust in what the system will (not) do. We envision a wide variety of situations where predictions about the future are beneficial to inhabitants of smart homes, such as debugging unintended behavior and managing conflicts by exception, and hope to spark a new generation of intelligible tools for ubiquitous environments.

Feedback is commonly used to explain what happened in an interface. What if questions, on the other hand, remain mostly unanswered. In this paper, we present the concept of enhanced widgets capable of visualizing their future state, which helps users to understand what will happen without committing to an action. We describe two approaches to extend GUI toolkits to support widget-level feedforward, and illustrate the usefulness of widget-level feedforward in a standardized interface to control the weather radar in commercial aircraft. In our evaluation, we found that users require less clicks to achieve tasks and are more confident about their actions when feedforward information was available. These findings suggest that widget-level feedforward is highly suitable in applications the user is unfamiliar with, or when high confidence is desirable.

Feedback and feedforward are two fundamental mechanisms that supports users' activities while interacting with computing devices. While feedback can be easily solved by providing information to the users following the triggering of an action, feedforward is much more complex as it must provide information before an action is performed. Fortunettes is a generic mechanism providing a systematic way of designing feedforward addressing both action and presentation problems. Including a feedforward mechanism significantly increases the complexity of the interactive application hardening developers' tasks to detect and correct defects. This paper proposes the use of an existing formal notation for describing the behavior of interactive applications and how to exploit that formal model to extend the behavior to offer feedforward. We use a small login example to demonstrate the process and the results.

We present key results of SCATE (Smart Computer-Aided Translation Environment). The project investigated algorithms, user interfaces and methods that can contribute to the development of more efficient tools for translation work.

Translation environments offer various translation aids to support professional translators. However, translation aids typically provide only limited justification for the translation suggestions they propose. In this paper we present Intellingo, a translation environment that explores intelligibility for translation aids, to enable more sensible usage of translation suggestions. We performed a comparative study between an intelligible version and a non-intelligible version of Intellingo. The results show that although adding intelligibility does not necessarily result in significant changes to the user experience, translators can better assess translation suggestions without a negative impact on their performance. Intelligibility is preferred by translators when the additional information it conveys benefits the translation process and when this information is not part of the translator's readily available knowledge.

We have developed an approach that presents patients with an intelligible, user-friendly yet correct visualisation to check progress and verify adherence to the prescribed physical exercise program. Integrated in a comprehensive, mobile self-monitoring app, this patient-centric approach facilitates keeping patients motivated and engaged while rehabilitating remotely.

We present Gestu-Wan, an intelligible gesture guidance system designed to support mid-air gesture-based interaction for walk-up-and-use displays. Although gesture-based interfaces have become more prevalent, there is currently very little uniformity with regard to gesture sets and the way gestures can be executed. This leads to confusion, bad user experiences and users who rather avoid than engage in interaction using mid-air gesturing. Our approach improves the visibility of gesture-based interfaces and facilitates execution of mid-air gestures without prior training. We compare Gestu-Wan with a static gesture guide, which shows that it can help users with both performing complex gestures as well as understanding how the gesture recognizer works.

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.

In contrast with design flaws that occur in user interfaces, design flaws in physical spaces have a much higher cost and impact. Software is in fact fairly easy to change and update in contrast with legacy physical constructions where updating their physical appearance is often not an option. We present the Feedforward Torch, a mobile projection system that targets the augmentation of legacy hardware with feedforward information. Feedforward explains users what the results of their action will be, and can thus be seen as the opposite of feedback. A first user study suggests that providing feedforward in these environments could improve their usability.