In the context of rising sedentary lifestyles, this paper investigates the efficacy of "Situated Artifacts"in promoting physical activity. We designed two artifacts that display users' physical activity data within their homes - one physical and one digital. We conducted a 9-week, counterbalanced, within-subject field study with N = 24 participants to assess the impact of these artifacts on physical activity, reflection, and motivation. We collected quantitative data on physical activity and administered daily and weekly questionnaires, employing individual Likert items and standardized instruments, as well as conducted interviews post-prototype usage. Our findings indicate that while both artifacts act as reminders for physical activity, the physical artifact was superior in terms of user engagement. The study revealed that this can be attributed to the higher perceived presence and, thereby, enhanced social interaction, which acts as a motivational source for activity. In this sense, situated artifacts gently nudge toward sustainable health behavior change.

Currently, interactive systems use physiological sensing to enable advanced functionalities. While eye tracking is a promising means to understand the user, eye tracking data inherently suffers from missing data due to blinks, which may result in reduced system performance. We conducted a literature review to understand how researchers deal with this issue. We uncovered that researchers often implemented their use-case-specific pipeline to overcome the issue, ranging from ignoring missing data to artificial interpolation. With these first insights, we run a large-scale analysis on 11 publicly available datasets to understand the impact of the various approaches on data quality and accuracy. By this, we highlight the pitfalls in data processing and which methods work best. Based on our results, we provide guidelines for handling eye tracking data for interactive systems. Further, we propose a standard data processing pipeline that allows researchers and practitioners to pre-process and standardize their data efficiently.

Physiological sensing enables us to use advanced adaptive functionalities through physiological data (e.g., eye tracking) to change conditions. In this work, we investigate the impact of infilling methods on LSTM models’ performance in handling missing eye tracking data, specifically during blinks and gaps in recording. We conducted experiments using recommended infilling techniques from previous work on an openly available eye tracking dataset and LSTM model structure. Our findings indicate that the infilling method significantly influences LSTM prediction accuracy. These results underscore the importance of standardized infilling approaches for enhancing the reliability and reproducibility of LSTM-based eye tracking applications on a larger scale. Future work should investigate the impact of these infilling methods in larger datasets to investigate generalizability.

Captions are a valuable scaffold for language learners, aiding comprehension and vocabulary acquisition.Past work has proposed enhancements such as keyword highlights for increased learning gains.However, little is known about learners' experience with enhanced captions, although this is critical for adoption in everyday life.We conducted a survey and focus group to elicit learner preferences and requirements and implemented a processing pipeline CCS Concepts• Applied

Recent research has shown that users increasingly seek meaning in technologies and that eudaimonic user experience (UX) is part of everyday encounters with technology. Yet, to date, there is no validated means to assess eudaimonic properties in interactive artefacts. We conceptualised, developed and validated a six-item questionnaire for measuring eudaimonic properties of technologies—the Eudaimonic Technology Experience Scale (ETES). Our scale includes two factors, which describe what aspects of a eudaimonic experience can be supported by technology: eudaimonic goals and self-knowlege. We consulted work in Human-Computer Interaction (HCI), psychology and philosophy to gather an initial set of concepts that could contribute to eudaimonic UX. We then built the scale based on expert interviews and exploratory factor analysis and verified its quality in a number of tests (confirmatory factor analysis, reliability and validity checks). ETES provides a standardised tool for identifying eudaimonic qualities in interactive systems and allows for rapidly comparing prototypes.

Eye tracking is the basis for many intelligent systems to predict user actions. A core challenge with eye-tracking data is that it inherently suffers from missing data due to blinks. Approaches such as intent prediction and user state recognition process gaze data using neural networks; however, they often have difficulty handling missing information. In an effort to understand how prior work dealt with missing data, we found that researchers often simply ignore missing data or adopt use-case-specific approaches, such as artificially filling in missing data. This inconsistency in handling missing data in eye tracking hinders the development of effective intelligent systems for predicting user actions and limits reproducibility. Furthermore, this can even lead to incorrect results. Thus, this lack of standardization calls for investigating possible solutions to improve the consistency and effectiveness of processing eye-tracking data for user action prediction.

A steadily increasing number of notifications, auditory, visual or ambient, competes for the user’s attention. Frequent unsuitable notifications can lead to a breakdown in efficiency and increase error rates. This paper compares the effectiveness, disruptiveness, and user experience of three different notification modalities: On-Object, ambient On-Environment, and On-Smartphone notifications. In a user study with 24 participants, we evaluate the three notification modalities during a cooking task where users were frequently exposed to notifications. Our results show that ambient On-Environment notifications minimize the time in which users can resume their primary task. Ambient On-Environment notifications were also perceived as least disrupting compared to the other two notification modalities. We discuss the design requirements for non-disruptive notifications in smart home environments and conclude with future strategies for notifying users at different urgency levels.

Smart speakers become increasingly ubiquitous in our homes. Consequently, we need to study how smart speakers affect the members of a household. Understanding the adoption of a smart speaker can assure it does not negatively influence the social dynamics within a household and create opportunities for further assistance. We deployed an Amazon Echo dot in nine households with 20 inhabitants who were new smart speaker users. We conducted multiple interviews, inquiring how a smart speaker was integrated into a household from day one. We investigated the development of social rules around using the device and how the smart speaker was appropriated. Users developed different strategies of using the device which altered social behaviours in some households. Further, we identified barriers and unmet requirements in introducing smart speakers to home environments. Our work contributes to an understanding of ubiquitous assistance for user groups at home.

While advances in mobile text entry enable smartphone users to type almost as fast as on hardware keyboards, text-heavy activities are still not widely adopted. One reason is the lack of shortcut mechanisms. In this article, we determine shortcuts for text-heavy activities, elicit shortcut gestures, implement them for a fully touch-sensitive smartphone, and conduct an evaluation with potential users. We found that experts perform around 800 keyboard shortcuts per day, which are not available on smartphones. Interviews revealed the lack of shortcuts as a major limitation that prevents mobile text editing. Therefore, we elicited gestures for the 22 most important shortcuts for smartphones that are touch-sensitive on the whole device surface. We implemented the gestures for a fully touch-sensitive smartphone using deep learning and evaluated them in realistic scenarios to gather feedback. We show that the developed prototype is perceived as intuitive and faster than recent commercial approaches.