We explore the metaphorical "daily memory pill"concept - a brief pictorial lifelog recap aimed at reviving and preserving memories. Leveraging psychological strategies, we explore the potential of such summaries to boost autobiographical memory. We developed an automated lifelogging memory prosthesis and a research protocol (Automated Memory Validation "AMV") for conducting privacy-aware, in-situ evaluations. We conducted a real-world lifelogging experiment for a month (n=11). We also designed a browser "Pixel Memories"for browsing one-week worth of lifelogs. The results suggest that daily timelapse summaries, while not yielding significant memory augmentation effects, also do not lead to memory degradation. Participants' confidence in recalled content remains unaltered, but the study highlights the challenge of users' overestimation of memory accuracy. Our core contributions, the AMV protocol and "Pixel Memories"browser, advance our understanding of memory augmentations and offer a privacy-preserving method for evaluating future ubicomp systems.

As science becomes increasingly data-intensive, the requirements for comprehensive Research Data Management (RDM) grow. This often overwhelms scientists, requiring more workload and training. The failure to conduct effective RDM leads to producing research artefacts that cannot be reproduced or reused. Past research placed high value on supporting data science workers, but focused mainly on data production, collection, processing, and sensemaking. In order to understand practices and needs of data science workers in relation to documentation, preservation, sharing, and reuse, we conducted a cross-domain study with 15 scientists and data managers from diverse scientific domains. We identified five core concepts which describe requirements, drivers, and boundaries in the development of commitment for RDM, essential for generating reproducible research artefacts: Practice, Adoption, Barriers, Education, and Impact. Based on those concepts, we introduce a stage-based model of personal RDM commitment evolution. The model can be used to drive the design of future systems that support a transition to open science. We discuss infrastructure, policies, and motivations involved at the stages and transitions in the model. Our work supports designers in understanding the constraints and challenges involved in designing for reproducibility in an age of data-driven science.

While the sport of golf offers unique opportunities for people of all ages to be active, it also requires mastering complex movements before it can be fully enjoyed. Extensive training is required to begin achieving any success, which may demotivate prospective players away from the sport. To aid beginner golfers and enable them to practice without supervision, we designed Subtletee---a system that enhances the bodily awareness of golfers by providing feedback on the position of their feet and elbows. We first identified key problems golf beginners face through expert interviews. We then evaluated different feedback modalities for Subtletee---visual, tactile and auditory in an experiment with 20 beginner golfers. We found that providing proprioception feedback increased the users' performance on the driving range, with tactile feedback providing the most benefits. Our work provides insights on delivering feedback during physical activity that requires high precision movements.

Navigating on large high-resolution displays (LHRDs) using devices built for traditional desktop computers can be strenuous and negatively impact user experience. As LHRDs transition to everyday use, new user-friendly interaction techniques need to be designed to capitalise on the potential offered by the abundant screen space on LHRDs. We conducted a study which compared mouse pointing and eye-tracker assisted pointing (MAGIC pointing) on LHRDs. In a controlled experiment with 35 participants, we investigated user performance in a one-dimensional pointing task and a map-based search task. We determined that MAGIC pointing had a lower throughput, but participants had the perception of higher performance. Our work contributes insights for the design of pointing techniques for LHRDs. The results indicate that the choice of technique is scenario-dependent which contrasts with desktop computers.

Previous work has shown that large high resolution displays (LHRDs) can enhance collaboration between users. As LHRDs allow free movement in front of the screen, an understanding of movement behavior is required to build successful interfaces for these devices. This paper presents Pac-Many; a multiplayer version of the classical computer game Pac-Man to study group dynamics when using LHRDs. We utilized smartphones as game controllers to enable free movement while playing the game. In a lab study, using a 4m × 1m LHRD, 24 participants (12 pairs) played Pac-Many in collaborative and competitive conditions. The results show that players in the collaborative condition divided screen space evenly. In contrast, competing players stood closer together to avoid benefits for the other player. We discuss how the nature of the task is important when designing and analyzing collaborative interfaces for LHRDs. Our work shows how to account for the spatial aspects of interaction with LHRDs to build immersive experiences.

While the proliferation of mobile devices has rendered mobile notifications ubiquitous, researchers are only slowly beginning to understand how these technologies affect everyday social interactions. In particular, the negative social influence of mobile interruptions remains unexplored from a methodological perspective. This paper contributes a mixed-method evaluation procedure for assessing the disruptive impact of mobile interruptions in conversation. The approach combines quantitative eye tracking, qualitative analysis, and a simulated conversation environment to enable fast assessment of disruptiveness. It is intended to be used as a part of an iterative interaction design process. We describe our approach in detail, present an example of its use to study a new call declining technique, and reflect upon the pros and cons of our approach.

Visual exploration of large image data sets is a common and widely performed task. With the ubiquity of cameras, personal image collections are growing. Even when working with large high-resolution displays, collections are typically too large to display all images. In this work, we present two full-body, hands-free zooming interaction techniques for image exploration on large high-resolution displays that allow users to freely move in front of the display. We compare these techniques with state-of-the-art image browsing, where a large number of images is presented in a static grid. The first technique selects and enlarges whole columns by full-body-tracking of the user. The second technique additionally uses the height of the users head to select a single picture as a focus point. The results show that novel interaction techniques and visualizations for image sets on large high-resolution displays are required and can enhance the advantages of such displays.

Large high-resolution displays (LHRDs) present new opportunities for interaction design in areas such as interactive visualization and data analytics. Design processes for graphical interfaces for LHRDs are still challenging. In this paper, we explore the design space of graphical interfaces for LHRDs by engaging in the creation of four prototypes for supporting office work. Specifically, we investigate how users can effectively manage application windows on LHRDs using four window alignment techniques: curved zooming, window grouping, window spinning and side pane navigation. We present the design and implementation of these window alignment techniques in a sample office application. Based on a mixed-methods user study of our prototypes, we contribute insights on designing future graphical interfaces for LHRDs. We show that potential users appreciate techniques, which enhance focus switching without changing the spatial relation between related windows.

Despite the proliferation of screens in everyday environments, providing values to remote displays for exploring complex data sets is still challenging. Enhanced input for remote screens can increase their utility and enable the construction of rich data-driven environments. Here, we investigate the opportunities provided by a variable movement resistance slider (VMRS), based on a motorized slide potentiometer. These devices are often used in professional soundboards as an effective way to provide discrete input. We designed, built and evaluated a remote input device using a VMRS that facilitates choosing a number on a discrete scale. By comparing our prototype to a traditional slide potentiometer and a software slider, we determined that for conditions where users are not looking at the slider, VMRS can offer significantly better performance and accuracy. Our findings contribute to the understanding of discrete input and enable building new interaction scenarios for large display environments.

Synchronous colocated collaborative sensemaking requires that analysts share their information and insights with each other. The challenge is to know when is the right time to share what information without disrupting the present state of analysis. This is crucial in ad-hoc sensemaking sessions with mobile devices because small screen space limits information display. To address these tensions, we propose and evaluate RAMPARTS - a spatially aware sensemaking system for collaborative crime analysis that aims to support faster information sharing, clue-finding, and analysis. We compare RAMPARTS to an interactive tabletop and a paper-based method in a controlled laboratory study. We found that RAMPARTS significantly decreased task completion time compared to paper, without affecting cognitive load or task completion time adversely compared to an interactive tabletop. We conclude that designing for ad-hoc colocated sensemaking on mobile devices could benefit from spatial awareness. In particular, spatial awareness could be used to identify relevant information, support diverse alignment styles for visual comparison, and enable alternative rhythms of sensemaking.

With the advent of lifelogging cameras the amount of personal video material is massively growing to an extent that easily overwhelms the user. To efficiently review lifelog data, we need well designed video navigation tools. In this paper, we analyze which cues are most beneficial for lifelog video navigation. We show that the information kind determines the most appropriate cue in single cue systems, but that multicue approaches are more appreciated. These findings can inspire to design video players with multiple navigation cues, including time, place, persons, and events for easier and more efficient lifelog video retrieval.

Pixel densities are increasing rapidly. We can observe this trend in particular for mobile devices like smartphones and tablets. Previous work revealed an effect of pixel density on subjective feedback and objective performance only for low resolution cathode ray tube screens. It is unclear if this effect persists for the four times higher pixel densities of current mobile devices. Therefore, we conducted a study to compare four pixel densities with 359, 180, 120, and 90 pixels per inch. While participants performed three tasks involving images, text and videos on a tablet, we measured perceived effort, perceived visual quality, task completion time, error rate, and body pose. Our results show that the effect of the pixel density highly depends on the content. We found that only for text, the four pixel densities have clearly different perceived media qualities. Pixel density seems to have a smaller effect on perceived media quality for images and videos and we found no effect on objective measures. Results show that text should be displayed in high resolution, while this is less important for images and videos.

Order Picking is not only one of the most important but also most mentally demanding and error-prone tasks in the industry. Both stationary and wearable systems have been introduced to facilitate this task. Existing stationary systems are not scalable because of the high cost and wearable systems have issues being accepted by the workers. In this paper, we introduce a mobile camera-projector cart called OrderPickAR, which combines the benefits of both stationary and mobile systems to support order picking through Augmented Reality. Our system dynamically projects in-situ picking information into the storage system and automatically detects when a picking task is done. In a lab study, we compare our system to existing approaches, i.e, Pick-by-Paper, Pick-by-Voice, and Pick-by-Vision. The results show that using the proposed system, order picking is almost twice as fast as other approaches, the error rate is decreased up to 9 times, and mental demands are reduced up to 50%.