注目の論文一覧

Techniques like electrical muscle stimulation (EMS) offer promise in assisting physical tasks by automating movements, e.g., shaking a spray-can or tapping a button. However, existing actuation systems improve the performance of a task that users are already focusing on (e.g., users are already focused on using the spray-can). Instead, we investigate whether these interactive-actuation systems (e.g., EMS) offer any benefits if they automate a task that happens in the background of the user's focus. Thus, we explored whether automating a repetitive movement via EMS would reduce mental workload while users perform parallel tasks (e.g., focusing on writing an essay while EMS stirs a pot of soup). In our study, participants performed a cognitively-demanding multitask aided by EMS (SplitBody condition) or performed by themselves (baseline). We found that with SplitBody performance increased (35% on both tasks, 18% on the non-EMS-automated task), physical-demand decreased (31%), and mental-workload decreased (26%).

Tagnoo is a computational plywood augmented with RFID tags, aimed at empowering woodworkers to effortlessly create room-scale smart environments. Unlike existing solutions, Tagnoo does not necessitate technical expertise or disrupt established woodworking routines. This battery-free and cost-effective solution seamlessly integrates computation capabilities into plywood, while preserving its original appearance and functionality. In this paper, we explore various parameters that can influence Tagnoo's sensing performance and woodworking compatibility through a series of experiments. Additionally, we demonstrate the construction of a small office environment, comprising a desk, chair, shelf, and floor, all crafted by an experienced woodworker using conventional tools such as a table saw and screws while adhering to established construction workflows. Our evaluation confirms that the smart environment can accurately recognize 18 daily objects and user activities, such as a user sitting on the floor or a glass lunchbox placed on the desk, with over 90% accuracy.

We propose a gaze-guiding method called MOSion to adjust the guiding strength reacted to observers’ motion based on a high-speed projector and the afterimage effect in the human vision system. Our method decomposes the target area into mosaic patterns to embed visual cues in the perceived images. The patterns can only direct the attention of the moving observers to the target area. The stopping observer can see the original image with little distortion because of light integration in the visual perception. The pre computation of the patterns provides the adaptive guiding effect without tracking devices and computational costs depending on the movements. The evaluation and the user study show that the mosaic decomposition enhances the perceived saliency with a few visual artifacts, especially in moving conditions. Our method embedded in white lights works in various situations such as planar posters, advertisements, and curved objects.

Augmented Reality (AR) excels at altering what we see but non-visual sensations are difficult to augment. To augment non-visual sensations in AR, we draw on the visual language of comic books. Synthesizing comic studies, we create a design space describing how to use comic elements (e.g., onomatopoeia) to depict non-visual sensations (e.g., hearing). To demonstrate this design space, we built eight demos, such as speed lines to make a user think they are faster and smell lines to make a scent seem stronger. We evaluate these elements in a qualitative user study (N=20) where participants performed everyday tasks with comic elements added as augmentations. All participants stated feeling a change in perception for at least one sensation, with perceived changes detected by between four participants (touch) and 15 participants (hearing). The elements also had positive effects on emotion and user experience, even when participants did not feel changes in perception.

We characterize and demonstrate how the principles of direct manipulation can improve interaction with large language models. This includes: continuous representation of generated objects of interest; reuse of prompt syntax in a toolbar of commands; manipulable outputs to compose or control the effect of prompts; and undo mechanisms. This idea is exemplified in DirectGPT, a user interface layer on top of ChatGPT that works by transforming direct manipulation actions to engineered prompts. A study shows participants were 50% faster and relied on 50% fewer and 72% shorter prompts to edit text, code, and vector images compared to baseline ChatGPT. Our work contributes a validated approach to integrate LLMs into traditional software using direct manipulation. Data, code, and demo available at https://osf.io/3wt6s.

Semantic typographic logos harmoniously blend typeface and imagery to represent semantic concepts while maintaining legibility. Conventional methods using spatial composition and shape substitution are hindered by the conflicting requirement for achieving seamless spatial fusion between geometrically dissimilar typefaces and semantics. While recent advances made AI generation of semantic typography possible, the end-to-end approaches exclude designer involvement and disregard personalized design. This paper presents TypeDance, an AI-assisted tool incorporating design rationales with the generative model for personalized semantic typographic logo design. It leverages combinable design priors extracted from uploaded image exemplars and supports type-imagery mapping at various structural granularity, achieving diverse aesthetic designs with flexible control. Additionally, we instantiate a comprehensive design workflow in TypeDance, including ideation, selection, generation, evaluation, and iteration. A two-task user evaluation, including imitation and creation, confirmed the usability of TypeDance in design across different usage scenarios.

This paper proposes a method for generating non-contact sensations using low-frequency sound waves without requiring user instrumentation. This method leverages the fundamental acoustic response of a confined space to produce predictable pressure spatial distributions at low frequencies, called modes. These modes can be used to produce sensations either throughout the body, in localized areas of the body, or within the body. We first validate the location and strength of the modes simulated by acoustic modeling. Next, a perceptual study is conducted to show how different frequencies produce qualitatively different sensations across and within the participants' bodies. The low-frequency sound offers a new way of delivering non-contact sensations throughout the body. The results indicate a high accuracy for predicting sensations at specific body locations.

We present MAF, a novel acoustic sensing approach that leverages the commodity hardware in bone conduction earphones for hand-to-face gesture interactions. Briefly, by shining audio signals with bone conduction earphones, we observe that these signals not only propagate along the surface of the human face but also dissipate into the air, creating an acoustic field that envelops the individual’s head. We conduct benchmark studies to understand how various hand-to-face gestures and human factors influence this acoustic field. Building on the insights gained from these initial studies, we then propose a deep neural network combined with signal preprocessing techniques. This combination empowers MAF to effectively detect, segment, and subsequently recognize a variety of hand-to-face gestures, whether in close contact with the face or above it. Our comprehensive evaluation based on 22 participants demonstrates that MAF achieves an average gesture recognition accuracy of 92% across ten different gestures tailored to users' preferences.

Communication in collaboration, especially synchronous, remote communication, is crucial to the success of task-specific goals. Insufficient or excessive forms of communication may lead to detrimental effects on task performance while increasing mental fatigue. However, identifying which combinations of communication modalities provide the most efficient transfer of information in collaborative settings will greatly improve collaboration. To investigate this, we developed a remote, synchronous, asymmetric VR collaborative assembly task application, where users play the role of either mentor or mentee, and were exposed to different combinations of three communication modalities: voice, gestures, and gaze. Through task-based experiments with 25 pairs of participants (50 individuals), we evaluated quantitative and qualitative data and found that gaze did not differ significantly from multiple combinations of communication modalities. Our qualitative results indicate that mentees experienced more difficulty and frustration in completing tasks than mentors, with both types of users preferring all three modalities to be present.

Procrastination is the deliberate postponing of tasks knowing that it will have negative consequences in the future. Despite the potentially serious impact on mental and physical health, research has just started to explore the potential of information systems to help students combat procrastination. Specifically, while existing learning systems increasingly employ elements of game design to transform learning into an enjoyable and purposeful adventure, little is known about the effects of gameful approaches to overcome procrastination in academic settings. This study advances knowledge on gamification to counter procrastination by conducting a mixed-methods study among higher education students. Our results shed light on usage patterns and outcomes of gamification on self-efficacy, self-control, and procrastination behaviors. The findings contribute to theory by providing a better understanding of the potential of gamification to tackle procrastination. Practitioners are supported by implications on how to design gamified learning systems to support learners in self-organized work.

Audio notifications provide users with an efficient way to access information beyond their current focus of attention. Current notification delivery methods, like phone ringtones, are primarily optimized for high noticeability, enhancing situational awareness in some scenarios but causing disruption and annoyance in others. In this work, we build on the observation that music listening is now a commonplace practice and present MARingBA, a novel approach that blends ringtones into background music to modulate their noticeability. We contribute a design space exploration of music-adaptive manipulation parameters, including beat matching, key matching, and timbre modifications, to tailor ringtones to different songs. Through two studies, we demonstrate that MARingBA supports content creators in authoring audio notifications that fit low, medium, and high levels of urgency and noticeability. Additionally, end users prefer music-adaptive audio notifications over conventional delivery methods, such as volume fading.

Children with ADHD can experience a wide variety of challenges related to self-regulation, which can lead to poor educational, health, and wellness outcomes. Technological interventions, such as mobile and wearable health systems, can support data collection and reflection about health status. However, little is known about how ADHD children interpret such data. We conducted a deployment study with 10 children, aged 10 to 15, for six weeks, during which they used a smartwatch in their homes. Results from observations and interviews during this study indicate that children with ADHD can interpret their own health data, particularly at the moment. However, as ADHD children develop more autonomy, smartwatch systems may require alternatives for data reflection that are interpretable and actionable for them. This work contributes to the scholarly discourse around health data visualization, particularly in considering implications for the design of health technologies for children with ADHD.

There is great potential for adapting Virtual Reality (VR) exergames based on a user's affective state. However, physical activity and VR interfere with physiological sensors, making affect recognition challenging. We conducted a study (n=72) in which users experienced four emotion inducing VR exergaming environments (happiness, sadness, stress and calmness) at three different levels of exertion (low, medium, high). We collected physiological measures through pupillometry, electrodermal activity, heart rate, and facial tracking, as well as subjective affect ratings. Our validated virtual environments, data, and analyses are openly available. We found that the level of exertion influences the way affect can be recognised, as well as affect itself. Furthermore, our results highlight the importance of data cleaning to account for environmental and interpersonal factors interfering with physiological measures. The results shed light on the relationships between physiological measures and affective states and inform design choices about sensors and data cleaning approaches for affective VR.

In crowded settings, the human brain can focus on speech from a target speaker, given prior knowledge of how they sound. We introduce a novel intelligent hearable system that achieves this capability, enabling target speech hearing to ignore all interfering speech and noise, but the target speaker. A naive approach is to require a clean speech example to enroll the target speaker. This is however not well aligned with the hearable application domain since obtaining a clean example is challenging in real world scenarios, creating a unique user interface problem. We present the first enrollment interface where the wearer looks at the target speaker for a few seconds to capture a single, short, highly noisy, binaural example of the target speaker. This noisy example is used for enrollment and subsequent speech extraction in the presence of interfering speakers and noise. Our system achieves a signal quality improvement of 7.01 dB using less than 5 seconds of noisy enrollment audio and can process 8 ms of audio chunks in 6.24 ms on an embedded CPU. Our user studies demonstrate generalization to real-world static and mobile speakers in previously unseen indoor and outdoor multipath environments. Finally, our enrollment interface for noisy examples does not cause performance degradation compared to clean examples, while being convenient and user-friendly. Taking a step back, this paper takes an important step towards enhancing the human auditory perception with artificial intelligence.

Countdowns and count-ups are very useful displays that explicitly show how long users should wait and also show the current processing states of a given task. Most countdowns or count-ups decrease or increase their digit every one second exactly, and most users have an implicit assumption that the digit changes every one second exactly. However, there are no studies that investigate how users perceive wait times with these countdowns and count-ups and that consider changing users' perception of time passing as shorter than the actual passage of time by means of countdowns and count-ups while taking into account such user assumptions. To clarify these issues, we first investigated how users perceive countdowns "from 3/5/10 to 0" and count-ups "from 0 to 3/5/10" that have different lengths of intervals from 800 to 1200 msec (Experiment 1). Next, on the basis of the results of Experiment 1, we explored a novel method for presenting countdowns to make users perceive the wait time as being shorter than the actual wait time (Experiment 2) and investigated whether such countdowns can be used in realistic applications or not (Experiment 3). As a result, we found that countdowns and count-ups that were "from 250 msec shorter to 10% longer" than 3, 5, or 10 sec were perceived as 3, 5, or 10 sec, respectively, and those "from 5 to 0" (their lengths were 5 sec) that first displayed extremely shorter intervals were perceived as being shorter than their actual length (5 sec). Finally, we confirmed the applicability and effectiveness of such displays in a realistic application. Thus, we strongly argue that these findings could become indispensable knowledge for researchers in this research field to reduce users' cognitive load during wait times.

This paper examines the role that enchantment plays in the design of AI things by constructing a taxonomy of design approaches that increase or decrease the perception of magic and enchantment. We start from the design discourse surrounding recent developments in AI technologies, highlighting specific interaction qualities such as algorithmic uncertainties and errors and articulating relations to the rhetoric of magic and supernatural thinking. Through analyzing and reflecting upon 52 students' design projects from two editions of a Master course in design and AI, we identify seven design principles and unpack the effects of each in terms of enchantment and disenchantment. We conclude by articulating ways in which this taxonomy can be approached and appropriated by design/HCI practitioners, especially to support exploration and reflexivity.

Mental health and wellbeing have become one of the significant challenges in global society, for which emotional regulation strategies hold the potential to offer a transversal approach to addressing them. However, the persistently declining adherence of patients to therapeutic interventions, coupled with the limited applicability of current technological interventions across diverse individuals and diagnoses, underscores the need for innovative solutions. We present ARCADIA, a Mixed-Reality platform strategically co-designed with therapists to enhance emotional regulation and self-compassion. ARCADIA comprises several gamified therapeutic activities, with a strong emphasis on fostering patient motivation. Through a dual study involving therapists and mental health patients, we validate the fully functional prototype of ARCADIA. Encouraging results are observed in terms of system usability, user engagement, and therapeutic potential. These findings lead us to believe that the combination of Mixed Reality and gamified therapeutic activities could be a significant tool in the future of mental health.

Task switching can occur frequently in daily routines with physical activity. In this paper, we introduce Spatial Gaze Markers, an augmented reality tool to support users in immediately returning to the last point of interest after an attention shift. The tool is task-agnostic, using only eye-tracking information to infer distinct points of visual attention and to mark the corresponding area in the physical environment. We present a user study that evaluates the effectiveness of Spatial Gaze Markers in simulated physical repair and inspection tasks against a no-marker baseline. The results give insights into how Spatial Gaze Markers affect user performance, task load, and experience of users with varying levels of task type and distractions. Our work is relevant to assist physical workers with simple AR techniques and render task switching faster with less effort.

Support for story character construction is as essential as characters are for stories. Building upon past research on early character construction stages, we explore how conversation with chatbot avatars embodying characters powered by more recent technologies could support the entire character construction process for creative writing. Through a user study (N=14) with creative writers, we examine thinking and usage patterns of CharacterMeet, a prototype system allowing writers to progressively manifest characters through conversation while customizing context, character appearance, voice, and background image. We discover that CharacterMeet facilitates iterative character construction. Specifically, participants, including those with more linear usual approaches, alternated between writing and personalized exploration through visualization of ideas on CharacterMeet while visuals and audio enhanced immersion. Our findings support research on iterative creative processes and the growing potential of personalizable generative AI creativity support tools. We present design implications for leveraging chatbot avatars in the creative writing process.

Electronic composites incorporate computing into physical materials, expanding the materiality of interactive systems for designers. In this research, we investigated acrylic as a substrate for electronics. Acrylic is valued for its visual and structural properties and is used widely in industrial design. We propose e-acrylic, an electronic composite that incorporates electronic circuits with acrylic sheets. Our approach to making this composite is centered on acrylic making practices that industrial designers are familiar with. We outline this approach systematically, including leveraging laser cutting to embed circuits into acrylic sheets, as well as different ways to shape e-acrylic into 3D objects. With this approach, we explored using e-acrylic to design interactive artifacts. We reflect on these applications to surface a design space of tangible interactive artifacts possible with this composite. We also discuss the implications of aligning electronics to an existing making practice, and working with the holistic materiality that e-acrylic embodies.

Large Language Models (LLMs) are notorious for blending fact with fiction and generating non-factual content, known as hallucinations. To address this challenge, we propose an interactive system that helps users gain insight into the reliability of the generated text. Our approach is based on the idea that the self-consistency of multiple samples generated by the same LLM relates to its confidence in individual claims in the generated texts. Using this idea, we design RELIC, an interactive system that enables users to investigate and verify semantic-level variations in multiple long-form responses. This allows users to recognize potentially inaccurate information in the generated text and make necessary corrections. From a user study with ten participants, we demonstrate that our approach helps users better verify the reliability of the generated text. We further summarize the design implications and lessons learned from this research for future studies of reliable human-LLM interactions.

Large Language Models (LLMs) offer promising opportunities in mental health domains, although their inherent complexity and low controllability elicit concern regarding their applicability in clinical settings. We present MindfulDiary, an LLM-driven journaling app that helps psychiatric patients document daily experiences through conversation. Designed in collaboration with mental health professionals, MindfulDiary takes a state-based approach to safely comply with the experts' guidelines while carrying on free-form conversations. Through a four-week field study involving 28 patients with major depressive disorder and five psychiatrists, we examined how MindfulDiary facilitates patients' journaling practice and clinical care. The study revealed that MindfulDiary supported patients in consistently enriching their daily records and helped clinicians better empathize with their patients through an understanding of their thoughts and daily contexts. Drawing on these findings, we discuss the implications of leveraging LLMs in the mental health domain, bridging the technical feasibility and their integration into clinical settings.

Voice Agents (VAs) are touted as being able to help users in complex tasks such as cooking and interacting as a conversational partner to provide information and advice while the task is ongoing. Through conversation analysis of 7 cooking sessions with a commercial VA, we identify challenges caused by a lack of contextual awareness leading to irrelevant responses, misinterpretation of requests, and information overload. Informed by this, we evaluated 16 cooking sessions with a wizard-led context-aware VA. We observed more fluent interaction between humans and agents, including more complex requests, explicit grounding within utterances, and complex social responses. We discuss reasons for this, the potential for personalisation, and the division of labour in VA communication and proactivity. Then, we discuss the recent advances in generative models and the VAs interaction challenges. We propose limited context awareness in VAs as a step toward explainable, explorable conversational interfaces.

Acoustic noise control or cancellation (ANC) is a commonplace component of modern audio headphones. ANC aims to actively mitigate disturbing environmental noise for a quieter and improved listening experience. ANC is digitally controlling frequency and amplitude characteristics of sound. Much less explored is visual noise and active visual noise control, which we address here. We first explore visual noise and scenarios in which visual noise arises based on findings from four workshops we conducted. We then introduce the concept of visual noise cancellation (VNC) and how it can be used to reduce identified effects of visual noise. In addition, we developed head-worn demonstration prototypes to practically explore the concept of active VNC with selected scenarios in a user study. Finally, we discuss the application of VNC, including vision augmentations that moderate the user's view of the environment to address perceptual needs and to provide augmented reality content.

We explore a range of different metaphors used for Voice User Interfaces (VUIs) by designers, end-users, manufacturers, and researchers using a novel framework derived from semi-structured interviews and a literature review. We focus less on the well-established idea of metaphors as a way for interface designers to help novice users learn how to interact with novel technology, and more on other ways metaphors can be used. We find that metaphors people use are contextually fluid, can change with the mode of conversation, and can reveal differences in how people perceive VUIs compared to other devices. Not all metaphors are helpful, and some may be offensive. Analyzing this broader class of metaphors can help understand, perhaps even predict problems. Metaphor analysis can be a low-cost tool to inspire design creativity and facilitate complex discussions about sociotechnical issues, enabling us to spot potential opportunities and problems in the situated use of technologies.

While effective for recording and sharing experiences, traditional in-context writing tools are relatively passive and unintelligent, serving more like instruments rather than companions. This reduces primary task (e.g., travel) enjoyment and hinders high-quality writing. Through formative study and iterative development, we introduce PANDALens, a Proactive AI Narrative Documentation Assistant built on an Optical See-Through Head Mounted Display that supports personalized documentation in everyday activities. PANDALens observes multimodal contextual information from user behaviors and environment to confirm interests and elicit contemplation, and employs Large Language Models to transform such multimodal information into coherent narratives with significantly reduced user effort. A real-world travel scenario comparing PANDALens with a smartphone alternative confirmed its effectiveness in improving writing quality and travel enjoyment while minimizing user effort. Accordingly, we propose design guidelines for AI-assisted in-context writing, highlighting the potential of transforming them from tools to intelligent companions.

Voice assistants (VAs) like Siri and Alexa are transforming human-computer interaction; however, they lack awareness of users' spatiotemporal context, resulting in limited performance and unnatural dialogue. We introduce GazePointAR, a fully-functional context-aware VA for wearable augmented reality that leverages eye gaze, pointing gestures, and conversation history to disambiguate speech queries. With GazePointAR, users can ask "what's over there?" or "how do I solve this math problem?" simply by looking and/or pointing. We evaluated GazePointAR in a three-part lab study (N=12): (1) comparing GazePointAR to two commercial systems, (2) examining GazePointAR's pronoun disambiguation across three tasks; (3) and an open-ended phase where participants could suggest and try their own context-sensitive queries. Participants appreciated the naturalness and human-like nature of pronoun-driven queries, although sometimes pronoun use was counter-intuitive. We then iterated on GazePointAR and conducted a first-person diary study examining how GazePointAR performs in-the-wild. We conclude by enumerating limitations and design considerations for future context-aware VAs.

Volumetric telepresence aims to create a shared space, allowing people in local and remote settings to collaborate seamlessly. Prior telepresence examples typically have asymmetrical designs, with volumetric capture in one location and objects in one format. In this paper, we present a volumetric telepresence mixed reality system that supports real-time, symmetrical, multi-user, partially distributed interactions, using objects in multiple formats, across multiple locations. We align two volumetric environments around a common spatial feature to create a shared workspace for remote and co-located people using objects in three formats: physical, virtual, and volumetric. We conducted a study with 18 participants over 6 sessions, evaluating how telepresence workspaces support spatial coordination and hybrid communication for co-located and remote users undertaking collaborative tasks. Our findings demonstrate the successful integration of remote spaces, effective use of proxemics and deixis to support negotiation, and strategies to manage interactivity in hybrid workspaces.

The whiteboard is essential for collaborative work. To preserve its physicality in remote collaboration, Mixed Reality (MR) can blend real whiteboards across distributed spaces. Going beyond reality, MR can further enable interactions like panning and zooming in a virtually reconfigurable infinite whiteboard. However, this reconfigurability conflicts with the sense of physicality. To address this tension, we introduce Blended Whiteboard, a remote collaborative MR system enabling reconfigurable surface blending across distributed physical whiteboards. Blended Whiteboard supports a unique collaboration style, where users can sketch on their local whiteboards but also reconfigure the blended space to facilitate transitions between loosely and tightly coupled work. We describe design principles inspired by proxemics; supporting users in changing between facing each other and being side-by-side, and switching between navigating the whiteboard synchronously and independently. Our work shows exciting benefits and challenges of combining physicality and reconfigurability in the design of distributed MR whiteboards.

Apple’s 1987 Knowledge Navigator video contains a vision of a sophisticated digital personal assistant, but the natural human-agent conversational dialog shown does not currently exist. To investigate why, the authors analyzed the video using three theoretical frameworks: the DiCoT framework, the HAT Game Analysis framework, and the Flows of Power framework. These were used to codify the human-agent interactions and classify the agent’s capabilities. While some barriers to creating such agents are technological, other barriers arise from privacy, social and situational factors, trust, and the financial business case. The social roles and asymmetric interactions of the human and agent are discussed in the broader context of HAT research, along with the need for a new term for these agents that does not rely on a human social relationship metaphor. This research offers designers of conversational agents a research roadmap to build more highly capable and trusted non-human teammates.

Gaze interaction presents a promising avenue in Virtual Reality (VR) due to its intuitive and efficient user experience. Yet, the depth control inherent in our visual system remains underutilized in current methods. In this study, we introduce FocusFlow, a hands-free interaction method that capitalizes on human visual depth perception within the 3D scenes of Virtual Reality. We first develop a binocular visual depth detection algorithm to understand eye input characteristics. We then propose a layer-based user interface and introduce the concept of "Virtual Window" that offers an intuitive and robust gaze-depth VR interaction, despite the constraints of visual depth accuracy and precision spatially at further distances. Finally, to help novice users actively manipulate their visual depth, we propose two learning strategies that use different visual cues to help users master visual depth control. Our user studies on 24 participants demonstrate the usability of our proposed virtual window concept as a gaze-depth interaction method. In addition, our findings reveal that the user experience can be enhanced through an effective learning process with adaptive visual cues, helping users to develop muscle memory for this brand-new input mechanism. We conclude the paper by discussing potential future research topics of gaze-depth interaction.

To support people at the end of life as they create management plans for their assets, planning approaches like estate planning are increasingly considering data. HCI scholarship has argued that developing more effective planning approaches to support end-of-life data planning is important. However, empirical research is needed to evaluate specific approaches and identify design considerations. To support end-of-life data planning, this paper presents a qualitative study evaluating two approaches to co-designing end-of-life data plans with participants. We find that asset-first inventory-centric approaches, common in material estate planning, may be ineffective when making plans for data. In contrast, heavily facilitated, mission-driven, relationship-centric approaches were more effective. This study expands previous research by validating the importance of starting end-of-life data planning with relationships and values, and highlights collaborative facilitation as a critical part of successful data planning approaches.

This paper explores the feasibility of deliberately designing VR motion that diverges from users’ physical movements to turn mundane, everyday transportation motion (e.g., metros, trains, and cars) into more entertaining VR motion experiences, in contrast to prior car-based VR approaches that synchronize VR motion to physical car movement exactly. To gain insight into users’ preferences for veering rate and veering direction for turning (left/right) and pitching (up/down) during the three phases of acceleration (accelerating, cruising, and decelerating), we conducted a formative, perceptual study (n=24) followed by a VR experience evaluation (n=18), all conducted on metro trains moving in a mundane, straight-line motion. Results showed that participants preferred relatively high veering rates, and preferred pitching upward during acceleration and downward during deceleration. Furthermore, while veering decreased comfort as expected, it significantly enhanced immersion (p<.01) and entertainment (p<.001) and the overall experience, with comfort being considered, was preferred by 89% of participants.

Despite the fact that spatio-temporal patterns of vibration, characterized as rhythmic compositions of tactile content, have exhibited an ability to elicit specific emotional responses and enhance the emotion conveyed by music, limited research has explored their underlying mechanism in regulating emotional states within the pre-sleep context. Aiming to investigate whether synergistic spatio-temporal tactile displaying of music can facilitate relaxation before sleep, we developed 16 vibration patterns and an audio-tactile prototype for presenting an ambient experience in a pre-sleep scenario. The stress-reducing effects were further evaluated and compared via a user experiment. The results showed that the spatio-temporal tactile display of music significantly reduced stress and positively influenced users' emotional states before sleep. Furthermore, our study highlights the therapeutic potential of incorporating quantitative and adjustable spatio-temporal parameters correlated with subjective psychophysical perceptions in the audio-tactile experience for stress management.

Social interaction is a crucial part of what it means to be human. Maintaining a healthy social life is strongly tied to positive outcomes for both physical and mental health. While we use personal informatics data to reflect on many aspects of our lives, technology-supported reflection for social interactions is currently under-explored. To address this, we first conducted an online survey (N=124) to understand how users want to be supported in their social interactions. Based on this, we designed and developed an app for users to track and reflect on their social interactions and deployed it in the wild for two weeks (N=25). Our results show that users are interested in tracking meaningful in-person interactions that are currently untraced and that an app can effectively support self-reflection on social interaction frequency and social load. We contribute insights and concrete design recommendations for technology-supported reflection for social interaction.

Sticking to daily plans is essential for achieving life goals but challenging in reality. This study presents a self-voice alarm as a novel daily goal reminder. Based on the strong literature on the psychological effects of self-voice, we developed a voice alarm system that reminds users of daily tasks to support their consistent task completion. Over the course of 14 days, participants (N = 63) were asked to complete daily vocabulary tasks when reminded by an alarm (i.e., self-voice vs. other-voice vs. beep sound alarm). The self-voice alarm elicited higher alertness and uncomfortable feelings while fostering more days of task completion and repetition compared to the beep sound alarm. Both self-voice and other-voice alarms increased users’ perceived usefulness of the alarm system. Leveraging both quantitative and qualitative approaches, we provide a practical guideline for designing voice alarm systems that will foster users’ behavioral changes to achieve daily goals.

Augmented Reality (AR) presents new opportunities for immersive storytelling. However, this immersiveness faces two main hurdles. First, AR's immersive quality is often confined to visual elements, such as pixels on a screen. Second, crafting immersive narratives is complex and generally beyond the reach of amateurs due to the need for advanced technical skills. We introduce Jigsaw, a system that empowers beginners to both experience and craft immersive stories, blending virtual and physical elements. Jigsaw uniquely combines mobile AR with readily available Internet-of-things (IoT) devices. We conducted a qualitative study with 20 participants to assess Jigsaw's effectiveness in both consuming and creating immersive narratives. The results were promising: participants not only successfully created their own immersive stories but also found the playback of three such stories deeply engaging. However, sensory overload emerged as a significant challenge in these experiences. We discuss design trade-offs and considerations for future endeavors in immersive storytelling involving AR and IoT.

Providing attention guidance, such as assisting in search tasks, is a prominent use for Augmented Reality. Typically, this is achieved by graphically overlaying geometrical shapes such as arrows. However, providing visual guidance can cause side effects such as attention tunnelling or scene occlusions, and introduce additional visual clutter. Alternatively, visual guidance can adjust saliency but this comes with different challenges such as hardware requirements and environment dependent parameters. In this work we advocate for using flicker as an alternative for real-world guidance using Augmented Reality. We provide evidence for the effectiveness of flicker from two user studies. The first compared flicker against alternative approaches in a highly controlled setting, demonstrating efficacy (N = 28). The second investigated flicker in a practical task, demonstrating feasibility with higher ecological validity (N = 20). Finally, our discussion highlights the opportunities and challenges when using flicker to provide real-world visual guidance using Augmented Reality.

Voice messages, by nature, prevent users from gauging the emotional tone without fully diving into the audio content. This hinders the shared emotional experience at the pre-retrieval stage. Research scarcely explored "Emotional Teasers"—pre-retrieval cues offering a glimpse into an awaiting message's emotional tone without disclosing its content. We introduce EmoWear, a smartwatch voice messaging system enabling users to apply 30 animation teasers on message bubbles to reflect emotions. EmoWear eases senders' choice by prioritizing emotions based on semantic and acoustic processing. EmoWear was evaluated in comparison with a mirroring system using color-coded message bubbles as emotional cues (N=24). Results showed EmoWear significantly enhanced emotional communication experience in both receiving and sending messages. The animated teasers were considered intuitive and valued for diverse expressions. Desirable interaction qualities and practical implications are distilled for future design. We thereby contribute both a novel system and empirical knowledge concerning emotional teasers for voice messaging.

There is a potential future where the content created by a human and an AI are indistinguishable. In this future, if you can't tell the difference, does it matter? We conducted a 3 (Assigned creator: human, human with AI assistance, AI) by 4 (Context: news, travel, health, and jokes) mixed-design experiment where participants evaluated human-written content that was presented as created by a human, a human with AI assistance, or an AI. We found that participants felt more negatively about the content creator and were less satisfied when they thought AI was used, but assigned creator had no effect on content judgments. We also identified five interpretations for how participants thought AI use affected the content creation process. Our work suggests that informing users about AI use may not have the intended effect of helping consumers make content judgments and may instead damage the relationship between creators and followers.

Spatial interaction relies on fast and accurate visual acquisition. In this work, we analyse how visual acquisition and tracking of targets presented in a head-mounted display is affected by the user moving linearly at walking and jogging paces. We study four reference frames in which targets can be presented: Head and World where targets are affixed relative to the head and environment, respectively; HeadDelay where targets are presented in the head coordinate system but follow head movement with a delay, and novel Path where targets remain at fixed distance in front of the user, in the direction of their movement. Results of our study in virtual reality demonstrate that the more stable the target is relative to the environment, the faster and more precise it can be fixated. The results have practical significance as head-mounted displays enable interaction during mobility, and in particular when eye tracking is considered as input.

In contrast to dialogue, wherein the exchange of completed messages occurs through turn-taking, synlogue is a mode of conversation characterized by co-creative processes, such as mutually complementing incomplete utterances and cooperative overlaps of backchannelings. Such co-creative conversations have the potential to alleviate social divisions in contemporary information environments. This study proposed the design concept of a synlogue based on literature in linguistics and anthropology and explored features that facilitate synlogic interactions in computer-mediated interfaces. Through an experiment, we focused on aizuchi, an important backchanneling element that drives synlogic conversation, and compared the speech and perceptual changes of participants when a bot dynamically uttered aizuchi or otherwise silent in a situation simulating an online video call. Consequently, we discussed the implications for interaction design based on our qualitative and quantitative analysis of the experiment. The synlogic perspective presented in this study is expected to facilitate HCI researchers to achieve more convivial forms of communication.

We investigate how path context, encompassing both comfort and attractiveness, shapes user experiences in outdoor locative storytelling using Augmented Reality (AR). Addressing a research gap that predominantly concentrates on indoor settings or narrative backdrops, our user-focused research delves into the interplay between perceived path context and locative AR storytelling on routes with diverse walkability levels. We examine the correlation and causation between narrative engagement, spatial presence, perceived workload, and perceived path context. Our findings show that on paths with reasonable path walkability, attractive elements positively influence the narrative experience. However, even in environments with assured narrative walkability, inappropriate safety elements can divert user attention to mobility, hindering the integration of real-world features into the narrative. These results carry significant implications for path creation in outdoor locative AR storytelling, underscoring the importance of ensuring comfort and maintaining a balance between comfort and attractiveness to enrich the outdoor AR storytelling experience.

Gaze-assisted interaction techniques enable intuitive selections without requiring manual pointing but can result in unintended selections, known as Midas touch. A confirmation trigger eliminates this issue but requires additional physical and conscious user effort. Brain-computer interfaces (BCIs), particularly passive BCIs harnessing anticipatory potentials such as the Stimulus-Preceding Negativity (SPN) - evoked when users anticipate a forthcoming stimulus - present an effortless implicit solution for selection confirmation. Within a VR context, our research uniquely demonstrates that SPN has the potential to decode intent towards the visually focused target. We reinforce the scientific understanding of its mechanism by addressing a confounding factor - we demonstrate that the SPN is driven by the user's intent to select the target, not by the stimulus feedback itself. Furthermore, we examine the effect of familiarly placed targets, finding that SPN may be evoked quicker as users acclimatize to target locations; a key insight for everyday BCIs.

Sensemaking in unfamiliar domains can be challenging, demanding considerable user effort to compare different options with respect to various criteria. Prior research and our formative study found that people would benefit from reading an overview of an information space upfront, including the criteria others previously found useful. However, existing sensemaking tools struggle with the "cold-start" problem -- not only requiring significant input from previous users to generate and share these overviews, but also that such overviews may turn out to be biased and incomplete. In this work, we introduce a novel system, Selenite, which leverages Large Language Models (LLMs) as reasoning machines and knowledge retrievers to automatically produce a comprehensive overview of options and criteria to jumpstart users' sensemaking processes. Subsequently, Selenite also adapts as people use it, helping users find, read, and navigate unfamiliar information in a systematic yet personalized manner. Through three studies, we found that Selenite produced accurate and high-quality overviews reliably, significantly accelerated users' information processing, and effectively improved their overall comprehension and sensemaking experience.

Tracking fine-grained finger movements with IMUs for continuous 2D-cursor control poses significant challenges due to limited sensing capabilities. Our findings suggest that finger-motion patterns and the inherent structure of joints provide beneficial physical knowledge, which lead us to enhance motion perception accuracy by integrating physical priors into ML models. We propose MouseRing, a novel ring-shaped IMU device that enables continuous finger-sliding on unmodified physical surfaces like a touchpad. A motion dataset was created using infrared cameras, touchpads, and IMUs. We then identified several useful physical constraints, such as joint co-planarity, rigid constraints, and velocity consistency. These principles help refine the finger-tracking predictions from an RNN model. By incorporating touch state detection as a cursor movement switch, we achieved precise cursor control. In a Fitts’ Law study, MouseRing demonstrated input efficiency comparable to touchpads. In real-world applications, MouseRing ensured robust, efficient input and good usability across various surfaces and body postures.

Many work domains include numerous interruptions, which can contribute to errors. We investigated the potential of augmented reality (AR) cues to facilitate primary task resumption after interruptions of varying lengths. Experiment 1 (N = 83) involved a computer-based primary task with a red AR arrow at the to-be-resumed task step which was placed via a gesture by the participants or automatically. Compared to no cue, both cues significantly reduced the resumption lag (i.e., the time between the end of the interruption and the resumption of the primary task) following long but not short interruptions. Experiment 2 (N = 38) involved a tangible sorting task, utilizing only the automatic cue. The AR cue facilitated task resumption compared to not cue after both short and long interruptions. We demonstrated the potential of AR cues in mitigating the negative effects of interruptions and make suggestions for integrating AR technologies for task resumption.

Changes in body perception influence behavior and emotion and can be induced through multisensory feedback. Auditory feedback to one's actions can trigger such alterations; however, it is unclear which individual factors modulate these effects. We employ and evaluate SoniWeight Shoes, a wearable device based on literature for altering one's weight perception through manipulated footstep sounds. In a healthy population sample across a spectrum of individuals (n=84) with varying degrees of eating disorder symptomatology, physical activity levels, body concerns, and mental imagery capacities, we explore the effects of three sound conditions (low-frequency, high-frequency and control) on extensive body perception measures (demographic, behavioral, physiological, psychological, and subjective). Analyses revealed an impact of individual differences in each of these dimensions. Besides replicating previous findings, we reveal and highlight the role of individual differences in body perception, offering avenues for personalized sonification strategies. Datasets, technical refinements, and novel body map quantification tools are provided.

The growing availability of generative AI technologies such as large language models (LLMs) has significant implications for creative work. This paper explores twofold aspects of integrating LLMs into the creative process – the divergence stage of idea generation, and the convergence stage of evaluation and selection of ideas. We devised a collaborative group-AI Brainwriting ideation framework, which incorporated an LLM as an enhancement into the group ideation process, and evaluated the idea generation process and the resulted solution space. To assess the potential of using LLMs in the idea evaluation process, we design an evaluation engine and compared it to idea ratings assigned by three expert and six novice evaluators. Our findings suggest that integrating LLM in Brainwriting could enhance both the ideation process and its outcome. We also provide evidence that LLMs can support idea evaluation. We conclude by discussing implications for HCI education and practice.

Highlighting text in a document is a common active reading strategy to remember information from documents. Learning theory suggests that for highlights to be effective, readers must be selective with what they choose to highlight. We investigate if an imposed user interface constraint limiting the number of highlighted words in a document reader can improve reading comprehension. A large-scale between-subjects experiment shows that constraining the number of words that can be highlighted leads to higher reading comprehension scores than highlighting nothing or highlighting an unlimited number of words. Our work empirically validates theories in psychology, which in turn enables several new research directions within HCI.