注目の論文一覧

We present the research area of personal dream informatics: studying the self-information systems that support dream engagement and communication between the dreaming self and the wakeful self. Through a survey study of 281 individuals primarily recruited from an online community dedicated to dreaming, we develop a dream-information systems view of dreaming and dream tracking as a type of self-information system. While dream-information systems are characterized by diverse tracking processes, motivations, and outcomes, they are universally constrained by the ephemeral dreamset - the short period of time between waking up and rapid memory loss of dream experiences. By developing a system dynamics model of dreaming we highlight feedback loops that serve as high leverage points for technology designers, and suggest a variety of design considerations for crafting technology that best supports dream recall, dream tracking, and dreamwork for nightmare relief and personal development.

Guided by the 12 principles of animation, stylization is a core 2D animation feature but has been utilized mainly by experienced animators. Although there are tools for stylizing 2D animations, creating stylized 3D animations remains a challenging problem due to the additional spatial dimension and the need for responsive actions like contact and collision. We propose a system that helps users create stylized casual 3D animations. A layered authoring interface is employed to balance between ease of use and expressiveness. Our surface level UI is a timeline sequencer that lets users add preset stylization effects such as squash and stretch and follow through to plain motions. Users can adjust spatial and temporal parameters to fine-tune these stylizations. These edits are propagated to our node-graph-based second level UI, in which the users can create custom stylizations after they are comfortable with the surface level UI. Our system also enables the stylization of interactions among multiple objects like force, energy, and collision. A pilot user study has shown that our fluid layered UI design allows for both ease of use and expressiveness better than existing tools.

This paper contributes to a taxonomy of augmented reality and robotics based on a survey of 460 research papers. Augmented and mixed reality (AR/MR) have emerged as a new way to enhance human-robot interaction (HRI) and robotic interfaces (e.g., actuated and shape-changing interfaces). Recently, an increasing number of studies in HCI, HRI, and robotics have demonstrated how AR enables better interactions between people and robots. However, often research remains focused on individual explorations and key design strategies, and research questions are rarely analyzed systematically. In this paper, we synthesize and categorize this research field in the following dimensions: 1) approaches to augmenting reality; 2) characteristics of robots; 3) purposes and benefits; 4) classification of presented information; 5) design components and strategies for visual augmentation; 6) interaction techniques and modalities; 7) application domains; and 8) evaluation strategies. We formulate key challenges and opportunities to guide and inform future research in AR and robotics.

Creating digital comics involves multiple stages, some creative and some menial. For example, coloring a comic requires a labor-intensive stage known as 'flatting,' or masking segments of continuous color, as well as creative shading, lighting, and stylization stages. The use of AI can automate the colorization process, but early efforts have revealed limitations---technical and UX---to full automation. Via a formative study of professionals, we identify flatting as a bottleneck and key target of opportunity for human-guided AI-driven automation. Based on this insight, we built FlatMagic, an interactive, AI-driven flat colorization support tool for Photoshop. Our user studies found that using FlatMagic significantly reduced professionals' real and perceived effort versus their current practice. While participants effectively used FlatMagic, we also identified potential constraints in interactions with AI and partially automated workflows. We reflect on implications for comic-focused tools and the benefits and pitfalls of intermediate representations and partial automation in designing human-AI collaboration tools for professionals.

Despite an abundance of carefully-crafted tutorials, trial-and-error remains many people’s preferred way to learn complex software. Yet, approaches to facilitate trial-and-error (such as tooltips) have evolved very little since the 1980s. While existing mechanisms work well for simple software, they scale poorly to large feature-rich applications. In this paper, we explore new techniques to support trial-and-error in complex applications. We identify key benefits and challenges of trial-and-error, and introduce a framework with a conceptual model and design space. Using this framework, we developed three techniques: ToolTrack to keep track of trial-and-error progress; ToolTrip to go beyond trial-and-error of single commands by highlighting related commands that are frequently used together; and ToolTaste to quickly and safely try commands. We demonstrate how these techniques facilitate trial-and-error, as illustrated through a proof-of-concept implementation in the CAD software Fusion 360. We conclude by discussing possible scenarios and outline directions for future research on trial-and-error.

How-to videos are often shot using camera angles that may not be optimal for learning motor tasks, with a prevalent use of third-person perspective. We present \textit{immersivePOV}, an approach to film how-to videos from an immersive first-person perspective using a head-mounted 360° action camera. immersivePOV how-to videos can be viewed in a Virtual Reality headset, giving the viewer an eye-level viewpoint with three Degrees of Freedom. We evaluated our approach with two everyday motor tasks against a baseline first-person perspective and a third-person perspective. In a between-subjects study, participants were assigned to watch the task videos and then replicate the tasks. Results suggest that immersivePOV reduced perceived cognitive load and facilitated task learning. We discuss how immersivePOV can also streamline the video production process for content creators. Altogether, we conclude that immersivePOV is an effective approach to film how-to videos for learners and content creators alike.

Now more than ever, people are using online platforms to communicate. Twitch, the foremost platform for live game streaming, offers many communication modalities. However, the platform lacks representation of social cues and signals of the audience experience, which are innately present in live events. To address this, we present a technology probe that captures the audience energy and response in a game streaming context. We designed a game and integrated a custom-communication modality—Commons Sense—in which the audience members' heart rates are sensed via webcam, averaged, and fed into a video game to affect sound, lighting, and difficulty. We conducted an `in-the-wild' evaluation with four Twitch streamers and their audience members (N=55) to understand how these groups interacted through Commons Sense. Audience members and streamers indicated high levels of enjoyment and engagement with Commons Sense, suggesting the potential of physiological interaction as a beneficial communication tool in live streaming.

We frequently utilize face emojis to express emotions in digital communication. But how wholly and precisely do such pictographs sample the emotional spectrum, and are there gaps to be closed? Our research establishes emoji intensity scales for seven basic emotions: happiness, anger, disgust, sadness, shock, annoyance, and love. In our survey (N = 1195), participants worldwide assigned emotions and intensities to 68 face emojis. According to our results, certain feelings, such as happiness or shock, are visualized by manifold emojis covering a broad spectrum of intensities. Other feelings, such as anger, have limited and only very intense representative visualizations. We further emphasize that the cultural background influences emojis' perception: for instance, linear-active cultures (e.g., UK, Germany) rate the intensity of such visualizations higher than multi-active (e.g., Brazil, Russia) or reactive cultures (e.g., Indonesia, Singapore). To summarize, our manuscript promotes future research on more expressive, culture-aware emoji design.

We introduce OVRlap, a VR interaction technique that lets the user perceive multiple places simultaneously from a first-person perspective. OVRlap achieves this by overlapping viewpoints. At any time, only one viewpoint is active, meaning that the user may interact with objects therein. Objects seen from the active viewpoint are opaque, whereas objects seen from passive viewpoints are transparent. This allows users to perceive multiple locations at once and easily switch to the one in which they want to interact. We compare OVRlap and a single-viewpoint technique in a study where 20 participants complete object-collection and monitoring tasks. We find that participants are significantly faster and move their head significantly less with OVRlap in both tasks. We propose how the technique might be improved through automated switching of the active viewpoint and intelligent viewpoint rendering.

Most video-based learning content is designed for desktops without considering mobile environments. We (1) investigate the gap between mobile learners’ challenges and video engineers’ considerations using mixed methods and (2) provide design guidelines for creating mobile-friendly MOOC videos. To uncover learners’ challenges, we conducted a survey (n=134) and interviews (n=21), and evaluated the mobile adequacy of current MOOCs by analyzing 41,722 video frames from 101 video lectures. Interview results revealed low readability and situationally-induced impairments as major challenges. The content analysis showed a low guideline compliance rate for key design factors. We then interviewed 11 video production engineers to investigate design factors they mainly consider. The engineers mainly focus on the size and amount of content while lacking consideration for color, complex images, and situationally-induced impairments. Finally, we present and validate guidelines for designing mobile-friendly MOOCs, such as providing adaptive and customizable visual design and context-aware accessibility support.

In recognition of food’s significant experiential pleasures, culinary practitioners and designers are increasingly exploring novel combinations of computing technologies and food. However, despite much creative endeavors, proposals and prototypes have so far largely maintained a traditional divide, treating food and technology as separate entities. In contrast, we present a “Research through Design” exploration of the notion of food as computational artifact: wherein food itself is the material of computation. We describe the Logic Bonbon, a dessert that can hydrodynamically regulate its flavor via a fluidic logic system. Through a study of experiencing the Logic Bonbon and reflection on our design practice, we offer a provisional account of how food as computational artifact can mediate new interactions through a novel approach to food-computation integration, that promotes an enriched future of Human-Food Interaction.

Reflecting on stress-related data is critical in addressing one’s mental health. Personal Informatics (PI) systems augmented by algorithms and sensors have become popular ways to help users collect and reflect on data about stress. While prediction algorithms in the PI systems are mainly for diagnostic purposes, few studies examine how the explainability of algorithmic prediction can support user-driven self-insight. To this end, we developed MindScope, an algorithm-assisted stress management system that determines user stress levels and explains how the stress level was computed based on the user's everyday activities captured by a smartphone. In a 25-day field study conducted with 36 college students, the prediction and explanation supported self-reflection, a process to re-establish preconceptions about stress by identifying stress patterns and recalling past stress levels and patterns that led to coping planning. We discuss the implications of exploiting prediction algorithms that facilitate user-driven retrospection in PI systems.

With the growing prevalence of affective computing applications, Automatic Emotion Recognition (AER) technologies have garnered attention in both research and industry settings. Initially limited to speech-based applications, AER technologies now include analysis of facial landmarks to provide predicted probabilities of a common subset of emotions (e.g., anger, happiness) for faces observed in an image or video frame. In this paper, we study the relationship between AER outputs and self-reports of affect employed by prior work, in the context of information work at a technology company. We compare the continuous observed emotion output from an AER tool to discrete reported affect obtained via a one-day combined tool-use and diary study (N=15). We provide empirical evidence showing that these signals do not completely align, and find that using additional workplace context only improves alignment up to 58.6%. These results suggest affect must be studied in the context it is being expressed, and observed emotion signal should not replace internal reported affect for affective computing applications.

Expertise-centric citizen science games (ECCSGs) can be powerful tools for crowdsourcing scientific knowledge production. However, to be effective these games must train their players on how to become experts, which is difficult in practice. In this study, we investigated the path to expertise and the barriers involved by interviewing players of three ECCSGs: Foldit, Eterna, and Eyewire. We then applied reflexive thematic analysis to generate themes of their experiences and produce a model of expertise and its barriers. We found expertise is constructed through a cycle of exploratory and social learning but prevented by instructional design issues. Moreover, exploration is slowed by a lack of polish to the game artifact, and social learning is disrupted by a lack of clear communication. Based on our analysis we make several recommendations for CSG developers, including: collaborating with professionals of required skill sets; providing social features and feedback systems; and improving scientific communication.

Mixed Reality is gaining interest as a platform for collaboration and focused work to a point where it may supersede current office settings in future workplaces. At the same time, we expect that interaction with physical objects and face-to-face communication will remain crucial for future work environments, which is a particular challenge in fully immersive Virtual Reality. In this work, we reconcile those requirements through a user's individual Asynchronous Reality, which enables seamless physical interaction across time. When a user is unavailable, e.g., focused on a task or in a call, our approach captures co-located or remote physical events in real-time, constructs a causality graph of co-dependent events, and lets immersed users revisit them at a suitable time in a causally accurate way. Enabled by our system AsyncReality, we present a workplace scenario that includes walk-in interruptions during a person's focused work, physical deliveries, and transient spoken messages. We then generalize our approach to a use-case agnostic concept and system architecture. We conclude by discussing the implications of an Asynchronous Reality for future offices.

Electronic health records in critical care medicine offer unprecedented opportunities for clinical reasoning and decision making. Paradoxically, these data-rich environments have also resulted in clinical decision support systems (CDSSs) that fit poorly into clinical contexts, and increase health workers cognitive load. In this paper, we introduce a novel approach to designing CDSSs that are embedded in clinical workflows, by presenting problem-based curated data views tailored for problem-driven discovery, team communication, and situational awareness. We describe the design and evaluation of one such CDSS, In-Sight, that embodies our approach and addresses the clinical problem of monitoring critically ill pediatric patients. Our work is the result of a co-design process, further informed by empirical data collected through formal usability testing, focus groups, and a simulation study with domain experts. We discuss the potential and limitations of our approach, and share lessons learned in our iterative co-design process.

This paper presents FlexHaptics, a design method for creating custom haptic input interfaces. Our approach leverages planar compliant structures whose force-deformation relationship can be altered by adjusting the geometries. Embedded with such structures, a FlexHaptics module exerts a fine-tunable haptic effect (i.e., resistance, detent, or bounce) along a movement path (i.e., linear, rotary, or ortho-planar). These modules can work separately or combine into an interface with complex movement paths and haptic effects. To enable the parametric design of FlexHaptic modules, we provide a design editor that converts user-specified haptic properties into underlying mechanical structures of haptic modules. We validate our approach and demonstrate the potential of FlexHaptic modules through six application examples, including a slider control for a painting application and a piano keyboard interface on touchscreens, a tactile low vision timer, VR game controllers, and a compound input device of a joystick and a two-step button.

Dynamically drawn content (e.g., handwritten text) in learning videos is believed to improve users’ engagement and learning over static powerpoint-based ones. However, evidence from existing literature is inconclusive. With the emergence of Optical Head-Mounted Displays (OHMDs), recent work has shown that video learning can be adapted for on-the-go scenarios. To better understand the role of dynamic drawing, we decoupled dynamically drawn text into two factors (font style and motion of appearance) and studied their impact on learning performance under two usage scenarios (while seated with desktop and walking with OHMD). We found that although letter-traced text was more engaging for some users, most preferred learning with typeface text that displayed the entire word at once and achieved better recall (46.7% higher), regardless of the usage scenarios. Insights learned from the studies can better inform designers on how to present text in videos for ubiquitous access.

In this paper, we investigate the use of shape-change for interaction with sound zones. A core challenge to designing interaction with sound zone systems is to support users' understanding of the unique spatial properties of sound zones. Shape-changing interfaces present new opportunities for addressing this. We present a structured investigation into this. We leveraged the knowledge of 12 sound experts to define a set of basic shapes and movements. Then, we constructed a prototype and conducted an elicitation study with 17 novice users, investigating the experience of these shapes and movements. Our findings show that physical visualizations of sound zones can be useful in supporting users' experience of sound zones. We present a framework of 4 basic pattern categories that prompt different sound zone experiences and outline further research directions for shape-change in supporting sound zone interaction.

A significant drawback of text passwords for end-user authentication is password reuse. We propose a novel approach to detect password reuse by leveraging gaze as well as typing behavior and study its accuracy. We collected gaze and typing behavior from 49 users while creating accounts for 1) a webmail client and 2) a news website. While most participants came up with a new password, 32% reported having reused an old password when setting up their accounts. We then compared different ML models to detect password reuse from the collected data. Our models achieve an accuracy of up to 87.7% in detecting password reuse from gaze, 75.8% accuracy from typing, and 88.75% when considering both types of behavior. We demonstrate that \revised{using gaze, password} reuse can already be detected during the registration process, before users entered their password. Our work paves the road for developing novel interventions to prevent password reuse.

Games are considered promising for engaging people with climate change. In virtual worlds, players can adopt empowering roles to mitigate greenhouse gas emissions and/or adapt to climate impacts. However, the lack of a comprehensive exploration of existing climate-related identities and actions prevents understanding their potential. Here, we analyze 80 video games and classify avatar identities, or expected player roles, into six types. Climate selves encourage direct life changes; climate citizens are easy to identify with and imitate; climate heroes are inspirational figures upholding environmental values; empowered individuals deliberate to avoid a tragedy of the commons; authorities should consider stakeholders and the environment; and faction leaders engage in bi- or multilateral relations. Adaptation is often for decision-making profiles, while empowered individuals, authorities, and faction leaders usually face conflicting objectives. We discuss our results in relation to avatar research and provide suggestions for researchers, designers, and educators.

Interacting with not only virtual but also real objects, or even virtual objects augmented by real objects becomes a trend of virtual reality (VR) interactions and is common in augmented reality (AR). However, current haptic shape rendering devices generally focus on feedback of virtual objects, and require the users to put down or take off those devices to perceive real objects. Therefore, we propose FingerX to render haptic shapes and enable users to touch, grasp and interact with virtual and real objects simultaneously. An extender on the fingertip extends to a corresponding height to support between the fingertip and the real objects or the hand, to render virtual shapes. A ring rotates and withdraws the extender behind the fingertip when touching real objects. By independently controlling four extenders and rings on each finger with the exception of the pinky finger, FingerX renders feedback in three common scenarios, including touching virtual objects augmented by real environments (e.g., a desk), grasping virtual objects augmented by real objects (e.g., a bottle) and grasping virtual objects in the hand. We conducted a shape recognition study to evaluate the recognition rates for these three scenarios and obtained an average recognition rate of 76.59% with shape visual feedback. We then performed a VR study to observe how users interact with virtual and real objects simultaneously and verify that FingerX significantly enhances VR realism, compared to current vibrotactile methods.

The effective width method of Fitts' law can normalize speed-accuracy biases in 1D target pointing tasks. However, in graphical user interfaces, more meaningful target shapes are rectangular. To empirically determine the best way to normalize the subjective biases, we ran remote and crowdsourced user experiments with three speed-accuracy instructions. We propose to normalize the speed-accuracy biases by applying the effective sizes to existing Fitts' law formulations including width W and height H. We call this target-size adjustment the bivariate effective width method. We found that, overall, Accot and Zhai's weighted Euclidean model using the effective width and height independently showed the best fit to the data in which the three instruction conditions were mixed (i.e., the time data measured in all instructions were analyzed with a single regression expression). Our approach enables researchers to fairly compare two or more conditions (e.g., devices, input techniques, user groups) with the normalized throughputs.

Microtransactions have become a major monetisation model in digital games, shaping their design, impacting their player experience, and raising ethical concerns. Research in this area has chiefly focused on loot boxes. This begs the question whether other microtransactions might actually be more relevant and problematic for players. We therefore conducted a content analysis of negative player reviews (n=801) of top-grossing mobile and desktop games to determine which problematic microtransactions are most prevalent and salient for players. We found that problematic microtransactions with mobile games featuring more frequent and different techniques compared to desktop games. Across both, players minded issues related to fairness, transparency, and degraded user experience, supporting prior theoretical work, and importantly take issue with monetisation-driven design as such. We identify future research needs on why microtransactions in particular spark this critique, and which player communities it may be more or less representative of.

Reach redirection is an illusion-based virtual reality (VR) interaction technique where a user’s virtual hand is shifted during a reach in order to guide their real hand to a physical location. Prior works have not considered the underlying sensorimotor processes driving redirection. In this work, we propose adapting a sensorimotor model for goal-directed reach to obtain a model for visually-redirected reach, specifically by incorporating redirection as a sensory bias in the state estimate used by a minimum jerk motion controller. We validate and then leverage this model to develop a Model Predictive Control (MPC) approach for reach redirection, enabling the real-time generation of spatial warping according to desired optimization criteria (e.g., redirection goals) and constraints (e.g., sensory thresholds). We illustrate this approach with two example criteria -- redirection to a desired point and redirection along a desired path -- and compare our approach against existing techniques in a user evaluation.

From creating input devices to rendering tangible information, the field of HCI is interested in using kinematic mechanisms to create human-computer interfaces. Yet, due to fabrication and design challenges, it is often difficult to create kinematic devices that are compact and have multiple reconfigurable motional degrees of freedom (DOFs) depending on the interaction scenarios. In this work, we combine compliant mechanisms (CMs) with tensioning cables to create dynamically reconfigurable kinematic mechanisms. The devices’ kinematics (DOFs) is enabled and determined by the layout of bendable rods. The additional cables function as on-demand motion constraints that can dynamically lock or unlock the mechanism’s DOFs as they are tightened or loosened. We provide algorithms and a design tool prototype to help users design such kinematic devices. We also demonstrate various HCI use cases including a kinematic haptic display, a haptic proxy, and a multimodal input device.

With the proliferation of shape-change research in affective computing, there is a need to deepen understandings of affective responses to shape-change display. Little research has focused on affective reactions to tactile experiences in shape-change, particularly in the absence of visual information. It is also rare to study response to the shape-change as it unfolds, isolated from a final shape-change outcome. We report on two studies on touch-affect associations, using the crossmodal ``Bouba-Kiki'' paradigm, to understand affective responses to shape-change as it unfolds. We investigate experiences with a shape-change gadget, as it moves between rounded (``Bouba'') and spiky (``Kiki'') forms. We capture affective responses via the circumplex model, and use a motion analysis approach to understand the certainty of these responses. We find that touch-affect associations are influenced by both the size and the frequency of the shape-change and may be modality-dependent, and that certainty in affective associations is influenced by association-consistency.

Text chat applications are an integral part of daily social and professional communication. However, messages sent over text chat applications do not convey vocal or nonverbal information from the sender, and detecting the emotional tone in text-only messages is challenging. In this paper, we explore the effects of speech balloon shapes on the sender-receiver agreement regarding the emotionality of a text message. We first investigated the relationship between the shape of a speech balloon and the emotionality of speech text in Japanese manga. Based on these results, we created a system that automatically generates speech balloons matching linear emotional arousal intensity by Auxiliary Classifier Generative Adversarial Networks (ACGAN). Our evaluation results from a controlled experiment suggested that the use of emotional speech balloons outperforms the use of emoticons in decreasing the differences between message senders' and receivers' perceptions about the level of emotional arousal in text messages.

Imagine in the future people comfortably wear augmented reality (AR) displays all day, how do we design interfaces that adapt to the contextual changes as people move around? In current operating systems, the majority of AR content defaults to staying at a fixed location until being manually moved by the users. However, this approach puts the burden of user interface (UI) transition solely on users. In this paper, we first ran a bodystorming design workshop to capture the limitations of existing manual UI transition approaches in spatially diverse tasks. Then we addressed these limitations by designing and evaluating three UI transition mechanisms with different levels of automation and controllability (low-effort manual, semi-automated, fully-automated). Furthermore, we simulated imperfect contextual awareness by introducing prediction errors with different costs to correct them. Our results provide valuable lessons about the trade-offs between UI automation levels, controllability, user agency, and the impact of prediction errors.

Making time estimates, such as how long a given task might take, frequently leads to inaccurate predictions because of an optimistic bias. Previous attempts to alleviate this bias, including decomposing the task into smaller components and listing potential surprises, have not shown any major improvement. This article builds on the premise that these procedures may have failed because they involve compound probabilities and mixture distributions which are difficult to compute in one's head. We hypothesize that predictive visualizations of such distributions would facilitate the estimation of task durations. We conducted a crowdsourced study in which 145 participants provided different estimates of overall and sub-task durations and we used these to generate predictive visualizations of the resulting mixture distributions. We compared participants' initial estimates with their updated ones and found compelling evidence that predictive visualizations encourage less optimistic estimates.

Flavobacteria, which can be found in marine environments, are able to grow in highly organized colonies producing vivid iridescent colorations. While much is known about the biology of these organisms, their design potential as responsive media in user interfaces has not been explored. Our paper aims at bridging this gap by providing insights into the type, degree, and duration of change in Flavobacteria’s expression, i.e., their living aesthetics. We present a tool to capture and characterize these changes concerning form, texture and iridescent color. To support the long-term study of their living aesthetics, we designed Flavorium. This bio-digital artifact provides the necessary habitat conditions for Flavobacteria to thrive for a month. Granting insights into the responsive behavior of this organism, this work presents a design space, vocabulary, and application concepts to inspire HCI and design scholars to investigate the complex temporal qualities of living media for future user interfaces.

By sensing how a user is holding a smartphone, adaptive user interfaces are possible such as those that automatically switch the displayed content and position of graphical user interface (GUI) components following how the phone is being held. We propose ReflecTouch, a novel method for detecting how a smartphone is being held by capturing images of the smartphone screen reflected on the cornea with a built-in front camera. In these images, the areas where the user places their fingers on the screen appear as shadows, which makes it possible to estimate the grasp posture. Since most smartphones have a front camera, this method can be used regardless of the device model; in addition, no additional sensor or hardware is required. We conducted data collection experiments to verify the classification accuracy of the proposed method for six different grasp postures, and the accuracy was 85%.

With recent developments in medical and psychiatric research surrounding pupillary response, cheap and accessible pupillometers could enable medical benefits from early neurological disease detection to measurements of cognitive load. In this paper, we introduce a novel smartphone-based pupillometer to allow for future development in clinical research surrounding at-home pupil measurements. Our solution utilizes a NIR front-facing camera for facial recognition paired with the RGB selfie camera to perform tracking of absolute pupil dilation with sub-millimeter accuracy. In comparison to a gold standard pupillometer during a pupillary light reflex test, the smartphone-based system achieves a median MAE of 0.27mm for absolute pupil dilation tracking and a median error of 3.52\% for pupil dilation change tracking. Additionally, we remotely deployed the system to older adults as part of a usability study that demonstrates promise for future smartphone deployments to remotely collect data in older, inexperienced adult users operating the system themselves.

Isolated silos of scientific research and the growing challenge of information overload limit awareness across the literature and hinder innovation. Algorithmic curation and recommendation, which often prioritize relevance, can further reinforce these informational "filter bubbles." In response, we describe Bridger, a system for facilitating discovery of scholars and their work. We construct a faceted representation of authors with information gleaned from their papers and inferred author personas, and use it to develop an approach that locates commonalities and contrasts between scientists to balance relevance and novelty. In studies with computer science researchers, this approach helps users discover authors considered useful for generating novel research directions. We also demonstrate an approach for displaying information about authors, boosting the ability to understand the work of new, unfamiliar scholars. Our analysis reveals that Bridger connects authors who have different citation profiles and publish in different venues, raising the prospect of bridging diverse scientific communities.

Desktop notifications should be noticeable but are also subject to a number of design choices, e.g. concerning their size, placement, or opacity. It is currently unknown, however, how these choices interact with the desktop background and their influence on noticeability. To address this limitation, we introduce a software tool to automatically synthesize realistically looking desktop images for major operating systems and applications. Using these images, we present a user study (N=34) to investigate the noticeability of notifications during a primary task. We are first to show that visual importance of the background at the notification location significantly impacts whether users detect notifications. We analyse the utility of visual importance to compensate for suboptimal design choices with respect to noticeability, e.g. small notification size. Finally, we introduce noticeability maps - 2D maps encoding the predicted noticeability across the desktop and inform designers how to trade-off notification design and noticeability.

Any active entity that shares space with people is interpreted as a social actor. Based on this notion, we explore how robots that integrate functional utility with a social role and character can integrate meaningfully into daily practice. Informed by interviews and observations, we designed a zoomorphic floor cleaning robot which playfully interacts with care home residents affected by dementia. A field study shows that playful interaction can facilitate the introduction of utilitarian robots in care homes, being nonthreatening and easy to make sense of. Residents previously reacted with distress to a Roomba robot, but were now amused by and played with our cartoonish cat robot or simply tolerated its presence. They showed awareness of the machine-nature of the robot, even while engaging in pretend-play. A playful approach to the design of functional robots can thus explicitly conceptualize such robots as social actors in their context of use.

FabricatINK explores the personal fabrication of irregularly-shaped low-power displays using electronic ink (E ink). E ink is a programmable bicolour material used in traditional form-factors such as E readers. It has potential for more versatile use within the scope of personal fabrication of custom-shaped displays, and it has the promise to be the pre-eminent material choice for this purpose. We appraise technical literature to identify properties of E ink, suited to fabrication. We identify a key roadblock, universal access to E ink as a material, and we deliver a method to circumvent this by upcycling broken electronics. We subsequently present a novel fabrication method for irregularly-shaped E ink displays. We demonstrate our fabrication process and E ink's versatility through ten prototypes showing different applications and use cases. By addressing E ink as a material for display fabrication, we uncover the potential for users to create custom-shaped truly bistable displays.

Often, emotional disorders are overlooked due to their lack of awareness, resulting in potential mental issues. Recent advances in sensing and inference technology provide a viable path to wearable facial-expression-based emotion recognition. However, most prior work has explored only laboratory settings and few platforms are geared towards end-users in everyday lives or provide personalized emotional suggestions to promote self-regulation. We present EmoGlass, an end-to-end wearable platform that consists of emotion detection glasses and an accompanying mobile application. Our single-camera-mounted glasses can detect seven facial expressions based on partial face images. We conducted a three-day out-of-lab study (N=15) to evaluate the performance of EmoGlass. We iterated on the design of the EmoGlass application for effective self-monitoring and awareness of users' daily emotional states. We report quantitative and qualitative findings, based on which we discuss design recommendations for future work on sensing and enhancing awareness of emotional health.

Sharing annotations encourages feedback, discussion, and knowledge passing among readers and can be beneficial for personal and public use. Prior augmented reality (AR) systems have expanded these benefits to both digital and printed documents. However, despite smartphone AR now being widely available, there is a lack of research about how to use AR effectively for interactive document annotation. We propose Dually Noted, a smartphone-based AR annotation system that recognizes the layout of structural elements in a printed document for real-time authoring and viewing of annotations. We conducted experience prototyping with eight users to elicit potential benefits and challenges within smartphone AR, and this informed the resulting Dually Noted system and annotation interactions with the document elements. AR annotation is often unwieldy, but during a 12-user empirical study our novel structural understanding component allows Dually Noted to improve precise highlighting and annotation interaction accuracy by 13%, increase interaction speed by 42%, and significantly lower cognitive load over a baseline method without document layout understanding. Qualitatively, participants commented that Dually Noted was a swift and portable annotation experience. Overall, our research provides new methods and insights for how to improve AR annotations for physical documents.

We present AirRacket, perceptual modeling and design of ungrounded, directional force feedback for virtual racket sports. Using compressed air propulsion jets to provide directional impact forces, we iteratively designed for three popular sports that span a wide range of force magnitudes: ping-pong, badminton, and tennis. To address the limited force magnitude of ungrounded force feedback technologies, we conducted a perception study which discovered the novel illusion that users perceive larger impact force magnitudes with longer impact duration, by an average factor of 2.57x. Through a series of formative, perceptual, and user experience studies with a combined total of 72 unique participants, we explored several perceptual designs using force magnitude scaling and duration scaling methods to expand the dynamic range of perceived force magnitude. Our user experience evaluation showed that perceptual designs can significantly improve realism and preference vs. physics-based designs for ungrounded force feedback systems.

The confusion matrix, a ubiquitous visualization for helping people evaluate machine learning models, is a tabular layout that compares predicted class labels against actual class labels over all data instances. We conduct formative research with machine learning practitioners at Apple and find that conventional confusion matrices do not support more complex data-structures found in modern-day applications, such as hierarchical and multi-output labels. To express such variations of confusion matrices, we design an algebra that models confusion matrices as probability distributions. Based on this algebra, we develop Neo, a visual analytics system that enables practitioners to flexibly author and interact with hierarchical and multi-output confusion matrices, visualize derived metrics, renormalize confusions, and share matrix specifications. Finally, we demonstrate Neo's utility with three model evaluation scenarios that help people better understand model performance and reveal hidden confusions.

Text-to-image generative models are a new and powerful way to generate visual artwork. However, the open-ended nature of text as interaction is double-edged; while users can input anything and have access to an infinite range of generations, they also must engage in brute-force trial and error with the text prompt when the result quality is poor. We conduct a study exploring what prompt keywords and model hyperparameters can help produce coherent outputs. In particular, we study prompts structured to include subject and style keywords and investigate success and failure modes of these prompts. Our evaluation of 5493 generations over the course of five experiments spans 51 abstract and concrete subjects as well as 51 abstract and figurative styles. From this evaluation, we present design guidelines that can help people produce better outcomes from text-to-image generative models.

Labeled datasets are essential for supervised machine learning. Various data labeling tools have been built to collect labels in different usage scenarios. However, developing labeling tools is time-consuming, costly, and expertise-demanding on software development. In this paper, we propose a conceptual framework for data labeling and OneLabeler based on the conceptual framework to support easy building of labeling tools for diverse usage scenarios. The framework consists of common modules and states in labeling tools summarized through coding of existing tools. OneLabeler supports configuration and composition of common software modules through visual programming to build data labeling tools. A module can be a human, machine, or mixed computation procedure in data labeling. We demonstrate the expressiveness and utility of the system through ten example labeling tools built with OneLabeler. A user study with developers provides evidence that OneLabeler supports efficient building of diverse data labeling tools.

Haptic Feedback is essential for lifelike Virtual Reality (VR) experiences. To provide a wide range of matching sensations of being touched or stroked, current approaches typically need large numbers of different physical textures. However, even advanced devices can only accommodate a limited number of textures to remain wearable. Therefore, a better understanding is necessary of how expectations elicited by different visualizations affect haptic perception, to achieve a balance between physical constraints and great variety of matching physical textures. In this work, we conducted an experiment (N=31) assessing how the perception of roughness is affected within VR. We designed a prototype for arm stroking and compared the effects of different visualizations on the perception of physical textures with distinct roughnesses. Additionally, we used the visualizations' real-world materials, no-haptics and vibrotactile feedback as baselines. As one result, we found that two levels of roughness can be sufficient to convey a realistic illusion.

Instructional videos for physical training have gained popularity in recent years among sport and fitness practitioners, due to the proliferation of affordable and ubiquitous forms of online training. Yet, learning movement this way poses challenges: lack of feedback and personalised instructions, and having to rely on personal imitation capacity to learn movements. We address some of these challenges by exploring visual cues’ potential to help people imitate movements from instructional videos. With a Research through Design approach, focused on strength training, we augmented an instructional video with different sets of visual cues: directional cues, body highlights, and metaphorical visualizations. We tested each set with ten practitioners over three recorded sessions, with follow-up interviews. Through thematic analysis, we derived insights on the effect of each set of cues for supporting movement learning. Finally, we generated design takeaways to inform future HCI work on visual cues for instructional training videos.

Today spectating and streaming virtual reality (VR) activities typically involves spectators viewing a 2D stream of the VR user’s view. Streaming 2D videos of the game play is popular and well-supported by platforms such as Twitch. However, the generic streaming of full 3D representations is less explored. Thus, while the VR player’s experience may be fully immersive, spectators are limited to 2D videos. This asymmetry lessens the overall experience for spectators, who themselves may be eager to spectate in VR. DreamStream puts viewers in the virtual environment of the VR application, allowing them to look “over the shoulder” of the VR player. Spectators can view streamed VR content immersively in 3D, independently explore the VR scene beyond what the VR player sees and ultimately cohabit the virtual environment alongside the VR player. For the VR player, DreamStream provides a spatial awareness of all their spectators. DreamStream retrofits and works with existing VR applications. We discuss the design and implementation of DreamStream, and carry out three qualitative informal evaluations. These evaluations shed light on the strengths and weakness of using DreamStream for the purpose of interactive spectating. Our participants found that DreamStream’s VR viewer interface offered increased immersion, and made it easier to communicate and interact with the VR player.

Diminished reality (DR) refers to the concept of removing content from a user's visual environment. While its implementation is becoming feasible, it is still unclear how users perceive and interact in DR-enabled environments and what applications it benefits. To address this challenge, we first conduct a formative study to compare user perceptions of DR and mediated reality effects (e.g., changing the color or size of target elements) in four example scenarios. Participants preferred removing objects through opacity reduction (i.e., the standard DR implementation) and appreciated mechanisms for maintaining a contextual understanding of diminished items (e.g., outlining). In a second study, we explore the user experience of performing tasks within DR-enabled environments. Participants selected which objects to diminish and the magnitude of the effects when performing two separate tasks (video viewing, assembly). Participants were comfortable with decreased contextual understanding, particularly for less mobile tasks. Based on the results, we define guidelines for creating general DR-enabled environments.

In many domains, strategic knowledge is documented and shared through checklists and handbooks. In software engineering, however, developers rarely share strategic knowledge for approaching programming problems, in contrast to other artifacts and despite its importance to productivity and success. To understand barriers to sharing, we simulated a programming strategy knowledge-sharing platform, asking experienced developers to articulate a programming strategy and others to use these strategies while providing feedback. Throughout, we asked strategy authors and users to reflect on the challenges they faced. Our analysis revealed that developers could share strategic knowledge. However, they struggled in choosing a level of detail and understanding the diversity of the potential audience. While authors required substantial feedback, users struggled to give it and authors to interpret it. Our results suggest that sharing strategic knowledge differs from sharing code and raises challenging questions about how knowledge-sharing platforms should support search and feedback.

Analysis of human motion data can reveal valuable insights about the utilization of space and interaction of humans with their environment. To support this, we present AvatAR, an immersive analysis environment for the in-situ visualization of human motion data, that combines 3D trajectories, virtual avatars of people’s movement, and a detailed representation of their posture. Additionally, we describe how to embed visualizations directly into the environment, showing what a person looked at or what surfaces they touched, and how the avatar’s body parts can be used to access and manipulate those visualizations. AvatAR combines an AR HMD with a tablet to provide both mid-air and touch interaction for system control, as well as an additional overview to help users navigate the environment. We implemented a prototype and present several scenarios to show that AvatAR can enhance the analysis of human motion data by making data not only explorable, but experienceable.

Textile interfaces enable designers to integrate unobtrusive media and smart home controls into furniture such as sofas. While the technical aspects of such controllers have been the subject of numerous research projects, the physical form factor of these controls has received little attention so far. This work investigates how general design properties, such as overall slider shape, raised vs. recessed sliders, and number and layout of tick marks, affect users' preferences and performance. Our first user study identified a preference for certain design combinations, such as recessed, closed-shaped sliders. Our second user study included performance measurements on variations of the preferred designs from study 1, and took a closer look at tick marks. Tick marks supported orientation better than slider shape. Sliders with at least three tick marks were preferred, and performed well. Non-uniform, equally distributed tick marks reduced the movements users needed to orient themselves on the slider.