注目の論文一覧

We present MARVIS, a conceptual framework that combines mobile devices and head-mounted Augmented Reality (AR) for visual data analysis. We propose novel concepts and techniques addressing visualization-specific challenges. By showing additional 2D and 3D information around and above displays, we extend their limited screen space. AR views between displays as well as linking and brushing are also supported, making relationships between separated visualizations plausible. We introduce the design process and rationale for our techniques. To validate MARVIS' concepts and show their versatility and widespread applicability, we describe six implemented example use cases. Finally, we discuss insights from expert hands-on reviews. As a result, we contribute to a better understanding of how the combination of one or more mobile devices with AR can benefit visual data analysis. By exploring this new type of visualization environment, we hope to provide a foundation and inspiration for future mobile data visualizations.

Passwords have become a ubiquitous part of our everyday lives, needed for every web-service and system. However, it is challenging to create safe and diverse alphanumeric passwords, and to recall them, imposing a cognitive burden on the user. Through consecutive experiments, we explored the movement space, affordances and interaction, and memorability of a tangible, handheld, embodied password. In this context, we found that: (1) a movement space of 200 mm × 200 mm is preferred; (2) each context has a perceived level of safety, which—together with the affordances and link to familiarity—influences how the password is performed. Furthermore, the artefact’s dimensions should be balanced within the design itself, with the user, and the context, but there is a trade-off between the perceived safety and ergonomics; and (3) the designed embodied passwords can be recalled for at least a week, with participants creating unique passwords which were reproduced consistently.

A common task for scatterplots is communicating trends in bivariate data. However, the ability of people to visually estimate these trends is under-explored, especially when the data violate assumptions required for common statistical models, or visual trend estimates are in conflict with statistical ones. In such cases, designers may need to intervene and de-bias these estimations, or otherwise inform viewers about differences between statistical and visual trend estimations. We propose data-driven mark orientation as a solution in such cases, where the directionality of marks in the scatterplot guide participants when visual estimation is otherwise unclear or ambiguous. Through a set of laboratory studies, we investigate trend estimation across a variety of data distributions and mark directionalities, and find that data-driven mark orientation can help resolve ambiguities in visual trend estimates.

Advertising posters are a commonly used form of information presentation to promote a product. Producing advertising posters often takes much time and effort of designers when confronted with abundant choices of design elements and layouts. This paper presents Vinci, an intelligent system that supports the automatic generation of advertising posters. Given the user-specified product image and taglines, Vinci uses a deep generative model to match the product image with a set of design elements and layouts for generating an aesthetic poster. The system also integrates online editing-feedback that supports users in editing the posters and updating the generated results with their design preference. Through a series of user studies and a Turing test, we found that Vinci can generate posters as good as human designers and that the online editing-feedback improves the efficiency in poster modification.

We present an observational study to compare co-located and situated real-time visualizations in basketball free-throw training. Our goal is to understand the advantages and concerns of applying immersive visualization to real-world skill-based sports training and to provide insights for designing AR sports training systems. We design both a situated 3D visualization on a head-mounted display and a 2D visualization on a co-located display to provide immediate visual feedback on a player's shot performance. Using a within-subject study design with experienced basketball shooters, we characterize user goals, report on qualitative training experiences, and compare the quantitative training results. Our results show that real-time visual feedback helps athletes refine subsequent shots. Shooters in our study achieve greater angle consistency with our visual feedback. Furthermore, AR visualization promotes an increased focus on body form in athletes. Finally, we present suggestions for the design of future sports AR studies.

Most mobile health apps employ data visualization to help people view their health and activity data, but these apps provide limited support for visual data exploration. Furthermore, despite its huge potential benefits, mobile visualization research in the personal data context is sparse. This work aims to empower people to easily navigate and compare their personal health data on smartphones by enabling flexible time manipulation with speech. We designed and developed Data@Hand, a mobile app that leverages the synergy of two complementary modalities: speech and touch. Through an exploratory study with 13 long-term Fitbit users, we examined how multimodal interaction helps participants explore their own health data. Participants successfully adopted multimodal interaction (i.e., speech and touch) for convenient and fluid data exploration. Based on the quantitative and qualitative findings, we discuss design implications and opportunities with multimodal interaction for better supporting visual data exploration on mobile devices.

Recent research in the area of immersive analytics demonstrated the utility of head-mounted augmented reality devices for visual data analysis. However, it can be challenging to use the by default supported mid-air gestures to interact with visualizations in augmented reality (e.g. due to limited precision). Touch-based interaction (e.g. via mobile devices) can compensate for these drawbacks, but is limited to two-dimensional input. In this work we present STREAM: Spatially-aware Tablets combined with Augmented Reality Head-Mounted Displays for the multimodal interaction with 3D visualizations. We developed a novel eyes-free interaction concept for the seamless transition between the tablet and the augmented reality environment. A user study reveals that participants appreciated the novel interaction concept, indicating the potential for spatially-aware tablets in augmented reality. Based on our findings, we provide design insights to foster the application of spatially-aware touch devices in augmented reality and research implications indicating areas that need further investigation.

In recent times, millions of people enjoy watching video gameplays at an eSports stadium or home. We seek a method that improves gameplay spectator or viewer experiences by presenting multisensory stimuli. Using a motion chair, we provide the motion effects automatically generated from the audiovisual stream to the viewers watching a first-person shooter (FPS) gameplay. The motion effects express the game character’s movement and gunfire action. We describe algorithms for the computation of such motion effects developed using computer vision techniques and deep learning. By a user study, we demonstrate that our method of providing motion effects significantly improves the viewing experiences of FPS gameplay. The contributions of this paper are with the motion synthesis algorithms integrated for FPS games and the empirical evidence for the benefits of experiencing multisensory gameplays.

We propose a novel type of olfactory device that creates a stereo-smell experience, i.e., directional information about the location of an odor, by rendering the readings of external odor sensors as trigeminal sensations using electrical stimulation of the user’s nasal septum. The key is that the sensations from the trigeminal nerve, which arise from nerve-endings in the nose, are perceptually fused with those of the olfactory bulb (the brain region that senses smells). As such, we propose that electrically stimulating the trigeminal nerve is an ideal candidate for stereo-smell augmentation/substitution that, unlike other approaches, does not require implanted electrodes in the olfactory bulb. To realize this, we engineered a self-contained device that users wear across their nasal septum. Our device outputs by stimulating the user’s trigeminal nerve using electrical impulses with variable pulse-widths; and it inputs by sensing the user’s inhalations using a photoreflector. It measures 10x23 mm and communicates with external gas sensors using Bluetooth. In our user study, we found the key electrical waveform parameters that enable users to feel an odor’s intensity (absolute electric charge) and direction (phase order and net charge). In our second study, we demonstrated that participants were able to localize a virtual smell source in the room by using our prototype without any previous training. Using these insights, our device enables expressive trigeminal sensations and could function as an assistive device for people with anosmia, who are unable to smell.

Emotional contagion is a phenomenon in which one's emotions are transmitted among individuals unconsciously by observing others' emotional expressions. In this paper, we propose a method for mediating people's emotions by triggering emotional contagion through artificial bodily changes such as pseudo tears. We focused on shedding tears because of the link to several emotions besides sadness. In addition, it is expected that shedding tears would induce emotional contagion because it is observable by others. We designed an eyeglasses-style wearable device, Teardrop glasses, that release water drops near the wearer's eyes. The drops flow down the cheeks and emulate real tears. The study revealed that artificial crying with pseudo tears increased sadness among both wearers and those observing them. Moreover, artificial crying attenuated happiness and positive feelings in observers. Our findings show that actual bodily changes are not necessary for inducing emotional contagion as artificial bodily changes are also sufficient.

Visual metaphors, which are widely used in graphic design, can deliver messages in creative ways by fusing different objects. The keys to creating visual metaphors are diverse exploration and creative combinations, which is challenging with conventional methods like image searching. To streamline this ideation process, we propose to use a mind-map-like structure to recommend and assist users to explore materials. We present MetaMap, a supporting tool which inspires visual metaphor ideation through multi-dimensional example-based exploration. To facilitate the divergence and convergence of the ideation process, MetaMap provides 1) sample images based on keyword association and color filtering; 2) example-based exploration in semantics, color, and shape dimensions; and 3) thinking path tracking and idea recording. We conduct a within-subject study with 24 design enthusiasts by taking a Pinterest-like interface as the baseline. Our evaluation results suggest that MetaMap provides an engaging ideation process and helps participants create diverse and creative ideas.

The access and growing ubiquity of digital fabrication has ushered in a celebration of creativity and ``making.'' However, the focus is often on the resulting static artifact or the creative process and tools to design it. We envision a post-making process that extends past these final static objects --- not just in their making but in their ``unmaking.'' By drawing from artistic movements such as Auto-Destructive Art, intentionally inverting well-established engineering principles of structurally sound designs, and safely misusing unstable materials, we demonstrate an important extension to making --- unmaking. In this paper, we provide designers with a new vocabulary of unmaking operations within standard 3D modeling tools. We demonstrate how such designs can be realized using a novel multi-material 3D printing process. Finally, we detail how unmaking allows designs to change over time, is an ally to sustainability and re-usability, and captures themes of ``aura,'' emotionality, and personalization.

The present study investigates how human subjects collaborate with a computer-mediated chatbot in creative idea generation tasks. In three text-based between-group studies, we tested whether the perceived identity (i.e.,whether the bot is perceived as a machine or as a human) or the conversational style of a teammate would moderate the outcomes of participants’ creative production. In Study 1, participants worked with either a chatbot or a human confederate. In Study 2, all participants worked with a human teammate but were informed that their partner was either a human or a chatbot. Conversely, all participants worked with a chatbot in Study 3, but were told the identity of their partner was either a chatbot or a human. We investigated differences in idea generation outcomes and found that participants consistently contributed more ideas and with ideas of higher quality when they perceived their teamworking partner as a bot. Furthermore, when the conversational style of the partner was robotic, participants with high anxiety in group communication reported greater creative self-efficacy in task performance. Finally, whether the perceived dominance of a partner and the pressure to come up with ideas during the task mediated positive outcomes of idea generation also depends on whether the conversational style of the bot partner was robot- or human-like. Based on our findings, we discussed implications for future design of artificial agents as active team players in collaboration tasks.

Geographic data visualisation on virtual globes is intuitive and widespread, but has not been thoroughly investigated. We explore two main design factors for quantitative data visualisation on virtual globes: i)~commonly used primitives (\textit{2D bar}, \textit{3D bar}, \textit{circle}) and ii)~the orientation of these primitives (\textit{tangential}, \textit{normal}, \textit{billboarded)}. We evaluate five distinctive visualisation idioms in a user study with 50 participants. The results show that aligning primitives tangentially on the globe’s surface decreases the accuracy of area-proportional circle visualisations, while the orientation does not have a significant effect on the accuracy of length-proportional bar visualisations. We also find that tangential primitives induce higher perceived mental load than other orientations. Guided by these results we design a novel globe visualisation idiom, \textit{Geoburst}, that combines a virtual globe and a radial bar chart. A preliminary evaluation reports potential benefits and drawbacks of the \textit{Geoburst} visualisation.

Visual design provides the backdrop to most of our interactions over the Internet, but has not received as much analytical attention as textual content. Combining computational with qualitative approaches, we investigate the growing concern that visual design of the World Wide Web has homogenized over the past decade. By applying computer vision techniques to a large data-set of representative websites images from 2003--2019, we show that designs have become significantly more similar since 2007, especially for page layouts where the average distance between sites decreased by over 30%. Synthesizing interviews from 11 experienced web design professionals with our computational analyses, we discuss causes of this homogenization including overlap in source code and libraries, color scheme standardization, and support for mobile devices. Our results seek to motivate future discussion of the factors that influence designers and their implications on the future trajectory of web design.

Digital biomarkers of mental health issues offer many advantages, including timely identification for early intervention, ongoing assessment during treatment, and reducing barriers to assessment stemming from geography, age, fear, or disparities in access to systems of care. Embedding digital biomarkers into games may further increase the reach of digital assessment. In this study, we explore game-based digital biomarkers for social anxiety, based on interaction with a non-player character (NPC). We show that social anxiety affects a player’s accuracy and their movement path in a gaming task involving an NPC. Further, we compared first versus third-person camera perspectives and the use of customized versus predefined avatars to explore the influence of common game interface factors on the expression of social anxiety through in-game movements. Our findings provide new insights about how game-based digital biomarkers can be effectively used for social anxiety, affording the benefits of early and ongoing digital assessment.

Interfaces designed to elicit empathy provide an opportunity for HCI with important pro-social outcomes. Recent research has demonstrated that perceiving expressive biosignals can facilitate emotional understanding and connection with others, but this work has been largely limited to visual approaches. We propose that hearing these signals will also elicit empathy, and test this hypothesis with sounding heartbeats. In a lab-based within-subjects study, participants (N=27) completed an emotion recognition task in different heartbeat conditions. We found that hearing heartbeats changed participants’ emotional perspective and increased their reported ability to “feel what the other was feeling.” From these results, we argue that auditory heartbeats are well-suited as an empathic intervention, and might be particularly useful for certain groups and use-contexts because of its musical and non-visual nature. This work establishes a baseline for empathic auditory interfaces, and offers a method to evaluate the effects of future designs.

Directly manipulating the timeline, such as scrubbing for thumbnails, is the standard way of controlling how-to videos. However, when how-to videos involve physical activities, people inconveniently alternate between controlling the video and performing the tasks. Adopting a voice user interface allows people to control the video with voice while performing the tasks with hands. However, naively translating timeline manipulation into voice user interfaces (VUI) results in temporal referencing (e.g. ``rewind 20 seconds''), which requires a different mental model for navigation and thereby limiting users' ability to peek into the content. We present RubySlippers, a system that supports efficient content-based voice navigation through keyword-based queries. Our computational pipeline automatically detects referenceable elements in the video, and finds the video segmentation that minimizes the number of needed navigational commands. Our evaluation (N=12) shows that participants could perform three representative navigation tasks with fewer commands and less frustration using RubySlippers than the conventional voice-enabled video interface.

In this paper, we present MIRIA, a Mixed Reality Interaction Analysis toolkit designed to support the in-situ visual analysis of user interaction in mixed reality and multi-display environments. So far, there are few options to effectively explore and analyze interaction patterns in such novel computing systems. With MIRIA, we address this gap by supporting the analysis of user movement, spatial interaction, and event data by multiple, co-located users directly in the original environment. Based on our own experiences and an analysis of the typical data, tasks, and visualizations used in existing approaches, we identify requirements for our system. We report on the design and prototypical implementation of MIRIA, which is informed by these requirements and offers various visualizations such as 3D movement trajectories, position heatmaps, and scatterplots. To demonstrate the value of MIRIA for real-world analysis tasks, we conducted expert feedback sessions using several use cases with authentic study data.

Two social motives are distinguished by Motive Disposition Theory: affiliation and power. Motives orient, select and energize our behaviour, suggesting that the choices of power-motivated individuals should be guided by power cues, such as the appearance of strength in a game character or avatar. In study 1 we demonstrate that participants were more likely to pick strong-looking Pokémon for a fight and cute Pokémon as a companion. In addition, we show that even when considering these contexts, the power motive predicts preferences for a powerful appearance, whereas affiliation does not. In study 2 we replicate the study 1 findings and distinguish between two ways to enact the power motive (prosocial and dominant power). We demonstrate that the dominance, but not the prosociality, facet drives the preference for strong-looking Pokémon. Our findings suggest that the need to influence others—the power motive—drives the choice for battle companions who symbolize strength.

Data physicalisations afford people the ability to directly interact with data using their hands, potentially achieving a more comprehensive understanding of a dataset. Due to their complex nature, the representation of graphs and networks could benefit from physicalisation, bringing the dataset from the digital world into the physical one. However, no empirical work exists investigating the effects physicalisations have upon comprehension as they relate to graph representations. In this work, we present initial design considerations for graph physicalisations, as well as an empirical study investigating differences in comprehension between virtual and physical representations. We found that participants perceived themselves as being more accurate via touch and sight (visual-haptic) than the graphical-only modality, and perceived a triangle count task as less difficult in visual-haptic than in the graphical-only modality. Additionally, we found that participants significantly preferred interacting with visual-haptic over other conditions, despite no significant effect on task time or error.

Virtual pets are an alternative to real pets, providing a substitute for people with allergies or preparing people for adopting a real pet. Recent advancements in mixed reality pave the way for virtual pets to provide a more natural and seamless experience for users. However, one key challenge is embedding environmental awareness into the virtual pet (e.g., identifying the food bowl's location) so that they can behave naturally in the real world. We propose a novel approach to synthesize virtual pet behaviors by considering scene semantics, enabling a virtual pet to behave naturally in mixed reality. Given a scene captured from the real world, our approach synthesizes a sequence of pet behaviors (e.g., resting after eating). Then, we assign each behavior in the sequence to a location in the real scene. We conducted user studies to evaluate our approach, which showed the efficacy of our approach in synthesizing natural virtual pet behaviors.

Tactile feedback is widely used to enhance realism in virtual reality (VR). When touching virtual objects, stiffness and roughness are common and obvious factors perceived by the users. Furthermore, when touching a surface with complicated surface structure, differences from not only stiffness and roughness but also surface height are crucial. To integrate these factors, we propose a pin-based handheld device, HairTouch, to provide stiffness differences, roughness differences, surface height differences and their combinations. HairTouch consists of two pins for the two finger segments close to the index fingertip, respectively. By controlling brush hairs' length and bending direction to change the hairs' elasticity and hair tip direction, each pin renders various stiffness and roughness, respectively. By further independently controlling the hairs' configuration and pins' height, versatile stiffness, roughness and surface height differences are achieved. We conducted a perception study to realize users' distinguishability of stiffness and roughness on each of the segments. Based on the results, we performed a VR experience study to verify that the tactile feedback from HairTouch enhances VR realism.

Acoustic levitation is gaining popularity as an approach to create physicalized mid-air content by levitating different types of levitation primitives. Such primitives can be independent particles or particles that are physically connected via threads or pieces of cloth to form shapes in mid-air. However, initialization (i.e., placement of such primitives in their mid-air target locations) currently relies on either manual placement or specialized ad-hoc implementations, which limits their practical usage. We present ArticuLev, an integrated pipeline that deals with the identification, assembly and mid-air placement of levitated shape primitives. We designed ArticuLev with the physical properties of commonly used levitation primitives in mind. It enables experiences that seamlessly combine different primitives into meaningful structures (including fully articulated animated shapes) and supports various levitation display approaches (e.g., particles moving at high speed). In this paper, we describe our pipeline and demonstrate it with heterogeneous combinations of levitation primitives.

Plots and tables are commonplace in today's data-driven world, and much research has been done on how to make these figures easy to read and understand. Often times, however, the information they contain conveys only the end result of a complex and subtle data analysis pipeline. This can leave the reader struggling to understand what steps were taken to arrive at a figure, and what implications this has for the underlying results. In this paper, we introduce datamations, which are animations designed to explain the steps that led to a given plot or table. We present the motivation and concept behind datamations, discuss how to programmatically generate them, and provide the results of two large-scale randomized experiments investigating how datamations affect people's abilities to understand potentially puzzling results compared to seeing only final plots and tables containing those results.

We present Pose-on-the-Go, a full-body pose estimation system that uses sensors already found in today’s smartphones. This stands in contrast to prior systems, which require worn or external sensors. We achieve this result via extensive sensor fusion, leveraging a phone’s front and rear cameras, the user-facing depth camera, touchscreen, and IMU. Even still, we are missing data about a user’s body (e.g., angle of the elbow joint), and so we use inverse kinematics to estimate and animate probable body poses. We provide a detailed evaluation of our system, benchmarking it against a professional-grade Vicon tracking system. We conclude with a series of demonstration applications that underscore the unique potential of our approach, which could be enabled on many modern smartphones with a simple software update.

This paper presents GestureMap, a visual analytics tool for gesture elicitation which directly visualises the space of gestures. Concretely, a Variational Autoencoder embeds gestures recorded as 3D skeletons on an interactive 2D map. GestureMap further integrates three computational capabilities to connect exploration to quantitative measures: Leveraging DTW Barycenter Averaging (DBA), we compute average gestures to 1) represent gesture groups at a glance; 2) compute a new consensus measure (variance around average gesture); and 3) cluster gestures with k-means. We evaluate GestureMap and its concepts with eight experts and an in-depth analysis of published data. Our findings show how GestureMap facilitates exploring large datasets and helps researchers to gain a visual understanding of elicited gesture spaces. It further opens new directions, such as comparing elicitations across studies. We discuss implications for elicitation studies and research, and opportunities to extend our approach to additional tasks in gesture elicitation.

Visual blends are an advanced graphic design technique to seamlessly integrate two objects into one. Existing tools help novices create prototypes of blends, but it is unclear how they would improve them to be higher fidelity. To help novices, we aim to add structure to the iterative improvement process. We introduce a method for improving prototypes that uses secondary design dimensions to explore a structured design space. This method is grounded in the cognitive principles of human visual object recognition. We present VisiFit – a computational design system that uses this method to enable novice graphic designers to improve blends with computationally generated options they can select, adjust, and chain together. Our evaluation shows novices can substantially improve 76% of blends in under 4 minutes. We discuss how the method can be generalized to other blending problems, and how computational tools can support novices by enabling them to explore a structured design space quickly and efficiently.

Natural language interfaces (NLIs) for data visualization are becoming increasingly popular both in academic research and in commercial software. Yet, there is a lack of empirical understanding of how people specify visualizations through natural language. We conducted an online study (N = 102), showing participants a series of visualizations and asking them to provide utterances they would pose to generate the displayed charts. From the responses, we curated a dataset of 893 utterances and characterized the utterances according to (1) their phrasing (e.g., commands, queries, questions) and (2) the information they contained (e.g., chart types, data aggregations). To help guide future research and development, we contribute this utterance dataset and discuss its applications toward the creation and benchmarking of NLIs for visualization.

Computer technology is often designed in technology hubs in Western countries, invariably making it "WEIRD", because it is based on the intuition, knowledge, and values of people who are Western, Educated, Industrialized, Rich, and Democratic. Developing technology that is universally useful and engaging requires knowledge about members of WEIRD and non-WEIRD societies alike. In other words, it requires us, the CHI community, to generate this knowledge by studying representative participant samples. To find out to what extent CHI participant samples are from Western societies, we analyzed papers published in the CHI proceedings between 2016-2020. Our findings show that 73% of CHI study findings are based on Western participant samples, representing less than 12% of the world's population. Furthermore, we show that most participant samples at CHI tend to come from industrialized, rich, and democratic countries with generally highly educated populations. Encouragingly, recent years have seen a slight increase in non-Western samples and those that include several countries. We discuss suggestions for further broadening the international representation of CHI participant samples.

In this paper, we present TexYZ, a method for rapid and effortless manufacturing of textile mutual capacitive sensors using a commodity embroidery machine. We use enameled wire as a bobbin thread to yield textile capacitors with high quality and consistency. As a consequence, we are able to leverage the precision and expressiveness of projected mutual capacitance for textile electronics, even when size is limited. Harnessing the assets of machine embroidery, we implement and analyze five distinct electrode patterns, examine the resulting electrical features with respect to geometrical attributes, and demonstrate the feasibility of two promising candidates for small-scale matrix layouts. The resulting sensor patches are further evaluated in terms of capacitance homogeneity, signal-to-noise ratio, sensing range, and washability. Finally, we demonstrate two use case scenarios, primarily focusing on continuous input with up to three degrees-of-freedom.

Training deep neural networks can generate non-descriptive error messages or produce unusual output without any explicit errors at all. While experts rely on tacit knowledge to apply debugging strategies, non-experts lack the experience required to interpret model output and correct Deep Learning (DL) programs. In this work, we identify DL debugging heuristics and strategies used by experts, and use them to guide the design of Umlaut. Umlaut checks DL program structure and model behavior against these heuristics; provides human-readable error messages to users; and annotates erroneous model output to facilitate error correction. Umlaut links code, model output, and tutorial-driven error messages in a single interface. We evaluated Umlaut in a study with 15 participants to determine its effectiveness in helping developers find and fix errors in their DL programs. Participants using Umlaut found and fixed significantly more bugs compared to a baseline condition.

Drone assisted navigation aids for supporting walking activities of visually impaired have been established in related work but fine-point object grasping tasks and the object localization in unknown environments still presents an open and complex challenge. We present a drone-based interface that provides fine-grain haptic feedback and thus physically guides them in hand-object localization tasks in unknown surroundings. Our research is built around community groups of blind or visually impaired (BVI) people, which provide in-depth insights during the development process and serve later as study participants. A pilot study infers users' sensibility to applied guiding stimuli forces and the different human-drone tether interfacing possibilities. In a comparative follow-up study, we show that our drone-based approach achieves greater accuracy compared to a current audio-based hand guiding system and delivers overall a more intuitive and relatable fine-point guiding experience.

We propose a nail-mounted foldable haptic device that provides tactile feedback to mixed reality (MR) environments by pressing against the user’s fingerpad when a user touches a virtual object. What is novel in our device is that it quickly tucks away when the user interacts with real-world objects. Its design allows it to fold back on top of the user’s nail when not in use, keeping the user’s fingerpad free to, for instance, manipulate handheld tools and other objects while in MR. To achieve this, we engineered a wireless and self-contained haptic device, which measures 24×24×41 mm and weighs 9.5 g. Furthermore, our foldable end-effector also features a linear resonant actuator, allowing it to render not only touch contacts (i.e., pressure) but also textures (i.e., vibrations). We demonstrate how our device renders contacts with MR surfaces, buttons, low- and high-frequency textures. In our first user study, we found that participants perceived our device to be more realistic than a previous haptic device that also leaves the fingerpad free (i.e., fingernail vibration). In our second user study, we investigated the participants’ experience while using our device in a real-world task that involved physical objects. We found that our device allowed participants to use the same finger to manipulate handheld tools, small objects, and even feel textures and liquids, without much hindrance to their dexterity, while feeling haptic feedback when touching MR interfaces.

Although social support is important for health and well-being, many young people are hesitant to reach out for support. The emerging uptake of chatbots for social and emotional purposes entails opportunities and concerns regarding non-human agents as sources of social support. To explore this, we invited 16 participants (16–21 years) to use and reflect on chatbots as sources of social support. Our participants first interacted with a chatbot for mental health (Woebot) for two weeks. Next, they participated in individual in-depth interviews. As part of the interview session, they were presented with a chatbot prototype providing information to young people. Two months later, the participants reported on their continued use of Woebot. Our findings provide in-depth knowledge about how young people may experience various types of social support—appraisal, informational, emotional, and instrumental support—from chatbots. We summarize implications for theory, practice, and future research.

Live streaming has become increasingly popular, with most streamers presenting their real-life appearance. However, Virtual YouTubers (VTubers), virtual 2D or 3D avatars that are voiced by humans, are emerging as live streamers and attracting a growing viewership in East Asia. Although prior research has found that many viewers seek real-life interpersonal interactions with real-person streamers, it is currently unknown what makes VTuber live streams engaging or how they are perceived differently than real-person streamers. We conducted an interview study to understand how viewers engage with VTubers and perceive the identities of the voice actors behind the avatars (i.e., Nakanohito). The data revealed that Virtual avatars bring unique performative opportunities which result in different viewer expectations and interpretations of VTuber behavior. Viewers intentionally upheld the disembodiment of VTuber avatars from their voice actors. We uncover the nuances in viewer perceptions and attitudes and further discuss the implications of VTuber practices to the understanding of live streaming in general.

There are many situations where using personal devices is not socially acceptable, or where nearby people present a privacy risk. For these situations, we explore the concept of hidden interaction techniques through two prototype applications. HiddenHaptics allows users to receive information through vibrotactile cues on a smartphone, and HideWrite allows users to write text messages by drawing on a dimmed smartwatch screen. We conducted three user studies to investigate whether, and how, these techniques can be used without being exposed. Our primary findings are (1) users can effectively hide their interactions while attending to a social situation, (2) users seek to interact when another person is speaking, and they also tend to hide the interaction using their body or furniture, and (3) users can sufficiently focus on the social situation despite their interaction, whereas non-users feel that observing the user hinders their ability to focus on the social activity.

Learning musical instruments using online instructional videos has become increasingly prevalent. However, pre-recorded videos lack the instantaneous feedback and personal tailoring that human tutors provide. In addition, existing video navigations are not optimized for instrument learning, making the learning experience encumbered. Guided by our formative interviews with guitar players and prior literature, we designed Soloist, a mixed-initiative learning framework that automatically generates customizable curriculums from off-the-shelf guitar video lessons. Soloist takes raw videos as input and leverages deep-learning based audio processing to extract musical information. This back-end processing is used to provide an interactive visualization to support effective video navigation and real-time feedback on the user’s performance, creating a guided learning experience. We demonstrate the capabilities and specific use-cases of Soloist within the domain of learning electric guitar solos using instructional YouTube videos. A remote user study, conducted to gather feedback from guitar players, shows encouraging results as the users unanimously preferred learning with Soloist over unconverted instructional videos.

3D printing has revolutionized rapid prototyping by speeding up the creation of custom-shaped objects. With the rise of multi-material 3Dprinters, these custom-shaped objects can now be made interactive in a single pass through passive conductive structures. However, connecting conventional electronics to these conductive structures often still requires time-consuming manual assembly involving many wires, soldering or gluing. To alleviate these shortcomings, we propose Oh, Snap!: a fabrication pipeline and interfacing concept to magnetically connect a 3D-printed object equipped with passive sensing structures to conventional sensing electronics. To this end, Oh, Snap! utilizes ferromagnetic and conductive 3D-printed structures, printable in a single pass on standard printers. We further present a proof-of-concept capacitive sensing board that enables easy and robust magnetic assembly to quickly create interactive 3D-printed objects. We evaluate Oh, Snap! by assessing the robustness and quality of the connection and demonstrate its broad applicability by a series of example applications.

Sketchnoting is a form of visual note taking where people listen to, synthesize, and visualize ideas from a talk or other event using a combination of pictures, diagrams, and text. Little is known about the design space of this kind of visual note taking. With an eye towards informing the implementation of digital equivalents of sketchnoting, inking, and note taking, we introduce a classification of sketchnote styles and techniques, with a qualitative analysis of 103 sketchnotes, and situated in context with six semi-structured follow up interviews. Our findings distill core sketchnote components (content, layout, structuring elements, and visual styling) and dimensions of the sketchnote design space, classifying levels of conciseness, illustration, structure, personification, cohesion, and craftsmanship. We unpack strategies to address particular note taking challenges, for example dealing with constraints of live drawings, and discuss relevance for future digital inking tools, such as recomposition, styling, and design suggestions.

Providing haptic feedback when manipulating virtual objects is an essential part of immersive virtual reality experiences; however, it is challenging to replicate all of an object’s properties and characteristics. We propose the use of visuo-haptic illusions alongside physical proxies to enhance the scope of proxy-based interactions with virtual objects. In this work, we focus on two manipulation techniques, linear translation and stretching across different distances, and investigate how much discrepancy between the physical proxy and the virtual object may be introduced without participants noticing. In a study with 24 participants, we found that manipulation technique and travel distance significantly affect the detection thresholds, and that visuo-haptic illusions impact performance and accuracy. We show that this technique can be used to enable functional proxy objects that act as stand-ins for multiple virtual objects, illustrating the technique through a showcase VR-DJ application.

Programming error messages play an important role in learning to program. The cycle of program input and error message response completes a loop between the programmer and the compiler/interpreter and is a fundamental interaction between human and computer. However, error messages are notoriously problematic, especially for novices. Despite numerous guidelines citing the importance of message readability, there is little empirical research dedicated to understanding and assessing it. We report three related experiments investigating factors that influence programming error message readability. In the first two experiments we identify possible factors, and in the third we ask novice programmers to rate messages using scales derived from these factors. We find evidence that several key factors significantly affect message readability: message length, jargon use, sentence structure, and vocabulary. This provides novel empirical support for previously untested long-standing guidelines on message design, and informs future efforts to create readability metrics for programming error messages.

Virtual environments (VEs) can create collaborative and social spaces, which are increasingly important in the face of remote work and travel reduction. Recent advances, such as more open and widely available platforms, create new possibilities to observe and analyse interaction in VEs. Using a custom instrumented build of Mozilla Hubs to measure position and orientation, we conducted an academic workshop to facilitate a range of typical workshop activities. We analysed social interactions during a keynote, small group breakouts, and informal networking/hallway conversations. Our mixed-methods approach combined environment logging, observations, and semi-structured interviews. The results demonstrate how small and large spaces influenced group formation, shared attention, and personal space, where smaller rooms facilitated more cohesive groups while larger rooms made small group formation challenging but personal space more flexible. Beyond our findings, we show how the combination of data and insights can fuel collaborative spaces' design and deliver more effective virtual workshops.

Eye gaze and head movement are attractive for hands-free 3D interaction in head-mounted displays, but existing interfaces afford only limited control. Radi-Eye is a novel pop-up radial interface designed to maximise expressiveness with input from only the eyes and head. Radi-Eye provides widgets for discrete and continuous input and scales to support larger feature sets. Widgets can be selected with Look & Cross, using gaze for pre-selection followed by head-crossing as trigger and for manipulation. The technique leverages natural eye-head coordination where eye and head move at an offset unless explicitly brought into alignment, enabling interaction without risk of unintended input. We explore Radi-Eye in three augmented and virtual reality applications, and evaluate the effect of radial interface scale and orientation on performance with Look & Cross. The results show that Radi-Eye provides users with fast and accurate input while opening up a new design space for hands-free fluid interaction.

Searching for relative mobile user interface (UI) design examples can aid interface designers in gaining inspiration and comparing design alternatives. However, finding such design examples is challenging, especially as current search systems rely on only text-based queries and do not consider the UI structure and content into account. This paper introduces VINS, a visual search framework, that takes as input a UI image (wireframe, high-fidelity) and retrieves visually similar design examples. We first survey interface designers to better understand their example finding process. We then develop a large-scale UI dataset that provides an accurate specification of the interface's view hierarchy (i.e., all the UI components and their specific location). By utilizing this dataset, we propose an object-detection based image retrieval framework that models the UI context and hierarchical structure. The framework achieves a mean Average Precision of 76.39\% for the UI detection and high performance in querying similar UI designs.

Reading is a fundamental activity to obtain information both in the real and the digital world. Virtual reality (VR) allows novel approaches for users to view, read, and interact with a text. However, for efficient reading, it is necessary to understand how a text should be displayed in VR without impairing the VR experience. Therefore, we conducted a study with 18 participants to investigate text presentation type and location in VR. We compared world-fixed, edge-fixed, and head-fixed text locations. Texts were displayed using Rapid Serial Visual Presentation (RSVP) or as a paragraph. We found that RSVP is a promising presentation type for reading short texts displayed in edge-fixed or head-fixed location in VR. The paragraph presentation type using world-fixed or edge-fixed location is promising for reading long text if movement in the virtual environment is not required. Insights from our study inform the design of reading interfaces for VR applications.

Online video games support the development of social relationships through gameplay. However, gamers often cannot cultivate and maintain relationships based on social factors such as personality when using in-game matchmaking services. To address this, teammate matching sites external to games have emerged and enable gamers to offer to play games with others in exchange for payment. The affordances of these services are different from other existing gamer social sites, e.g., live streaming. Interviews were conducted with 16 dedicated users on Bixin, one of China’s largest paid teammate matching sites, to examine user motivations, practices, and perceptions. The interviews found that gamers selected paid teammates on Bixin using different criteria compared to in-game matchmaking services and emphasized the importance of real-life characteristics such as voice. To maintain connections, paid teammates often also extended communication to external communication services such as WeChat. Although most gamers expected to communicate with paid teammates as if they were friends, very few reported building real friendships with their matched counterparts.

Recent advances in deep generative neural networks have made it possible for artificial intelligence to actively collaborate with human beings in co-creating novel content (e.g. music, art). While substantial research focuses on (individual) human-AI collaborations, comparatively less research examines how AI can play a role in human-human collaborations during co-creation. In a qualitative lab study, we observed 30 participants (15 pairs) compose a musical phrase in pairs, both with and without AI. Our findings reveal that AI may play important roles in influencing human social dynamics during creativity, including: 1) implicitly seeding a common ground at the start of collaboration, 2) acting as a psychological safety net in creative risk-taking, 3) providing a force for group progress, 4) mitigating interpersonal stalling and friction, and 5) altering users' collaborative and creative roles. This work contributes to the future of generative AI in social creativity by providing implications for how AI could enrich, impede, or alter creative social dynamics in the years to come.

A commitment device, an attempt to bind oneself for a successful goal achievement, has been used as an effective strategy to promote behavior change. However, little is known about how commitment devices are used in the wild, and what aspects of commitment devices are related to goal achievements. In this paper, we explore a large-scale dataset from stickK, an online behavior change support system that provides both financial and social commitments. We characterize the patterns of behavior change goals (e.g., topics and commitment setting) and then perform a series of multilevel regression analyses on goal achievements. Our results reveal that successful goal achievements are largely dependent on the configuration of financial and social commitment devices, and a mixed commitment setting is considered beneficial. We discuss how our findings could inform the design of effective commitment devices, and how large-scale data can be leveraged to support data-driven goal elicitation and customization.

Drones are rapidly populating human spaces, yet little is known about how these flying robots are perceived and understood by humans. Recent works suggested that their acceptance is predicated upon their sociability. This paper explores the use of facial expressions to represent emotions on social drones. We leveraged design practices from ground robotics and created a set of rendered robotic faces that convey basic emotions. We evaluated individuals' response to these emotional facial expressions on drones in two empirical studies (N = 98, N = 98). Our results demonstrate that individuals accurately recognize five drone emotional expressions, as well as make sense of intensities within emotion categories. We describe how participants were emotionally affected by the drone, showed empathy towards it, and created narratives to interpret its emotions. As a consequence, we formulate design recommendations for social drones and discuss methodological insights on the use of static versus dynamic stimuli in affective robotics studies.