Smart home inhabitants can specify trigger-condition-action rules to control the home's behavior. As the number of rules and their complexity grow, however, so does the probability of issues such as inconsistencies and redundancies. These can lead to unintended behavior, including security vulnerabilities and wasted resources, which harms the inhabitants' trust in the system. Existing approaches to handle unintended behavior typically require inhabitants to define all-encompassing, permanent solutions by modifying the rules. Although this is fitting in certain situations, some unforeseen situations might occur. We argue that the user always must have the last word to avoid unwanted behaviors, without altering the overall behavior. With FortClash, we present an approach to predict many different types of unintended behavior, and contribute four novel mechanisms to mediate them that rely on making one-time exceptions. With FortClash, inhabitants gain a new tool to deal with unintended behavior in the short-term that is compatible with existing long-term approaches such as editing rules.

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Within our work, we apply context-awareness to determine how AR/VR technology should adapt instructions based on the context to suit user needs. We focus on situations where the user must carry out a complex manual activity that requires additional information to be present during the activity to achieve the desired result. To this end, the emphasis is on activities that require fine-motor skills and in-depth expertise and training, for which XR is a powerful tool to support and guide users performing these tasks. The contexts we detect include user intentions, environmental conditions, and activity progressions. Our work builds on these contexts with the main focus on determining how XR should adapt for the end-user from a usability perspective. The feedback we request from ISMAR consists of input in detection, usability, and simulation categories, together with how to balance these categories to create real-time and user-friendly systems. The next steps of our work will consider how to content should adjust based on the cognitive load, activity space, and environmental conditions.

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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.

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Notifications have become a core component of the smart-phone as our ubiquitous companion. Many of these only require minimal interaction, for which the smartwatch is a helpful companion device. However, its design and placement is influenced by its traditional ancestors. For applications where the user is constrained because of a specific usage situation, or performs tasks with both hands simultaneously, interaction with the smartwatch can be cumbersome. In this paper, we propose a wearable armstrap for minimal interaction in long-lived tasks. Placed around the elbow, it is outside the hands' proximal working space which reduces interference. Its flexible e-ink display provides screen space to provide overview information at minimal energy consumption for longer uptime. We designed the wearable for a professional use-case, meaning that is can easily be placed above protective clothing as its flexible round shape easily adjusts to various diameters. Capacitive touch sensing allows gesture input even under rough conditions, e.g., with gloves.

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In mobile tele-rehabilitation applications for Coronary Artery Disease (CAD) patients, behavior change plays a central role in influencing better therapy adherence and prevention of disease recurrence. However, creating sustainab le behavior chan ge that holds a beneficial impact over a prolonged period of time remains an important challenge. In this paper we discuss various models and frameworks related to persuas ion and behav ior chan ge, and investigate how to incorporate these with a multidisciplinary user-centered design approach for creating a mobile tele-rehabilitation application. By implementing different concepts that contribute to behavior change and applying a set of distinct persuasive design patterns, we were able to translate the high-level goals of behavior theory into a mobile application that explicitly incorporates behavior change techniques and also offers a good overall user experience. We evaluated our system, HeartHab, in a lab setting and show that our approach leads to a high user acceptance and willingness to use the system in daily activities.

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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.

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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.

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