注目の論文一覧

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.

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.

While Virtual Reality (VR) systems can present virtual elements such as notifications anywhere, designing them so they are not missed by or distracting to users is highly challenging for content creators. To address this challenge, we introduce a novel approach to predict the noticeability of virtual elements. It computes the visual saliency distribution of what users see, and analyzes the temporal changes of the distribution with respect to the dynamic virtual elements that are animated. The computed features serve as input for a long short-term memory (LSTM) model that predicts whether a virtual element will be noticed. Our approach is based on data collected from 24 users in different VR environments performing tasks such as watching a video or typing. We evaluate our approach (n = 12), and show that it can predict the timing of when users notice a change to a virtual element within 2.56 sec compared to a ground truth, and demonstrate the versatility of our approach with a set of applications. We believe that our predictive approach opens the path for computational design tools that assist VR content creators in creating interfaces that automatically adapt virtual elements based on noticeability.

Robots are embodied agents that act under several sources of uncertainty. When assisting humans in a collaborative task, robots need to communicate their uncertainty to help inform decisions. In this study, we examine the use of visualising a robot’s uncertainty in a high-stakes assisted decision-making task. In particular, we explore how different modalities of uncertainty visualisations (graphical display vs. the robot’s embodied behaviour) and confidence levels (low, high, 100%) conveyed by a robot affect the human decision-making and perception during a collaborative task. Our results show that these visualisations significantly impact how participants arrive to their decision as well as how they perceive the robot’s transparency across the different confidence levels. We highlight potential trade-offs and offer implications for robot-assisted decision-making. Our work contributes empirical insights on how humans make use of uncertainty visualisations conveyed by a robot in a critical robot-assisted decision-making scenario.

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.

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.

This paper reports on a research through design (RtD) inquiry into public perceptions of transparency of Internet of Things (IoT) sensors increasingly deployed within urban neighborhoods as part of smart city programs. In particular, we report on the results of three participatory design workshops during which 40 New York City residents used physical signage as a medium for materializing transparency concerns about several sensors. We found that people’s concerns went beyond making sensors more transparent but instead sought to reveal the technology’s interconnected social, political, and economic processes. Building from these findings, we highlight the opportunities to move from treating transparency as an object to treating it as an ongoing activity. We argue that this move opens opportunities for designers and policy-makers to provide meaningful and actionable transparency of smart cities.

In this study, we explore the effectiveness of persuasive messages endorsing the adoption of a privacy protection technology (IoT Inspector) tailored to individuals' regulatory focus (promotion or prevention). We explore if and how regulatory fit (i.e., tuning the goal-pursuit mechanism to individuals' internal regulatory focus) can increase persuasion and adoption. We conducted a between-subject experiment (N = 236) presenting participants with the IoT Inspector in gain ("Privacy Enhancing Technology"---PET) or loss ("Privacy Preserving Technology"---PPT) framing. Results show that the effect of regulatory fit on adoption is mediated by trust and privacy calculus processes: prevention-focused users who read the PPT message trust the tool more. Furthermore, privacy calculus favors using the tool when promotion-focused individuals read the PET message. We discuss the contribution of understanding the cognitive mechanisms behind regulatory fit in privacy decision-making to support privacy protection.

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.

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.

With their generative capabilities, large language models (LLMs) have transformed the role of technological writing assistants from simple editors to writing collaborators. Such a transition emphasizes the need for understanding user perception and experience, such as balancing user intent and the involvement of LLMs across various writing domains in designing writing assistants. In this study, we delve into the less explored domain of personal writing, focusing on the use of LLMs in introspective activities. Specifically, we designed DiaryMate, a system that assists users in journal writing with LLM. Through a 10-day field study (N=24), we observed that participants used the diverse sentences generated by the LLM to reflect on their past experiences from multiple perspectives. However, we also observed that they are over-relying on the LLM, often prioritizing its emotional expressions over their own. Drawing from these findings, we discuss design considerations when leveraging LLMs in a personal writing practice.

While social media data is a valuable source for inferring human behavior, its in-practice utility hinges on extraneous factors. Notable is the ``observer effect,'' where awareness of being monitored can alter people's social media use. We present a causal-inference study to examine this phenomenon on the longitudinal Facebook use of 300+ participants who voluntarily shared their data spanning an average of 82 months before and 5 months after study enrollment. We measured deviation from participants' expected social media use through time series analyses. Individuals with high cognitive ability and low neuroticism decreased posting immediately after enrollment, and those with high openness increased posting. The sharing of self-focused content decreased, while diverse topics emerged. We situate the findings within theories of self-presentation and self-consciousness. We discuss the implications of correcting observer effect in social media data-driven measurements, and how this phenomenon shines light on the ethics of these measurements.

Designing and using technologies to support Non-Pharmacological Interventions (NPI) for People with Dementia (PwD) has drawn increasing attention in HCI, with the potential expectations of higher user engagement and positive outcomes. Yet, technologies for NPI can only be valuable if practitioners successfully incorporate them into their ongoing intervention practices beyond a limited research period. Currently, we know little about how practitioners experience and perceive these technologies in practical NPI for PwD. In this paper, we investigate this question through observations of five in-person NPI activities and interviews with 11 therapists and 5 caregivers. Our findings elaborate the practical NPI workflow process and characteristics, and practitioners’ attitudes, experiences, and perceptions to technology-mediated NPI in practice. Generally, our participants emphasized practical NPI is a complex and professional practice, needing fine-grained, personalized evaluation and planning, and the practical executing process is situated, and multi-stakeholder collaborative. Yet, existing technologies often fail to consider these specific characteristics, which leads to limitations in practical effectiveness or sustainable use. Drawing on our findings, we discuss the possible implications for designing more useful and practical NPI intervention technologies.

Reminiscence is known to play an important part in helping to mitigate the effects of dementia. Within the HCI community, work has typically focused on supporting reminiscence at an individual or social level but less attention has been given to supporting reminiscence in residential care settings. This lack of research became particularly apparent during the COVID pandemic when traditional forms of reminiscence involving physical artefacts and face-to-face interactions became especially challenging. In this paper we report on the design, development and evaluation of a reminiscence system, deployed in a residential care home over a two-year-period that included the pandemic. Mnemosyne comprises a pervasive display network and a browser-based application whose adoption and use we explored using a mixed methods approach. Our findings offer insights that will help shape the development and evaluation of future systems, particularly those that use pervasive displays to support unsupervised reminiscence.

The widespread use of Large Language Model (LLM)-based conversational agents (CAs), especially in high-stakes domains, raises many privacy concerns. Building ethical LLM-based CAs that respect user privacy requires an in-depth understanding of the privacy risks that concern users the most. However, existing research, primarily model-centered, does not provide insight into users' perspectives. To bridge this gap, we analyzed sensitive disclosures in real-world ChatGPT conversations and conducted semi-structured interviews with 19 LLM-based CA users. We found that users are constantly faced with trade-offs between privacy, utility, and convenience when using LLM-based CAs. However, users' erroneous mental models and the dark patterns in system design limited their awareness and comprehension of the privacy risks. Additionally, the human-like interactions encouraged more sensitive disclosures, which complicated users' ability to navigate the trade-offs. We discuss practical design guidelines and the needs for paradigm shifts to protect the privacy of LLM-based CA users.

Mobile health applications for weight maintenance offer self-monitoring as a tool to empower users to achieve health goals (e.g., losing weight); yet maintaining consistent self-monitoring over time proves challenging for users. These apps use push notifications to help increase users’ app engagement and reduce long-term attrition, but they are often ignored by users due to appearing at inopportune moments. Therefore, we analyzed whether delivering push notifications based on time alone or also considering user context (e.g., current activity) affected users’ engagement in a weight maintenance app, in a 4-week in-the-wild study with 30 participants. We found no difference in participants’ overall (across the day) self-monitoring frequency between the two conditions, but in the context-based condition, participants responded faster and more frequently to notifications, and logged their data more timely (as eating/exercising occurs). Our work informs the design of notifications in weight maintenance apps to improve their efficacy in promoting self-monitoring.

Increasing encounters between people and autonomous service robots may lead to conflicts due to mismatches between human expectations and robot behaviour. This interactive online study (N = 335) investigated human-robot interactions at an elevator, focusing on the effect of communication and behavioural expectations on participants' acceptance and compliance. Participants evaluated a humanoid delivery robot primed as either submissive or assertive. The robot either matched or violated these expectations by using a command or appeal to ask for priority and then entering either first or waiting for the next ride. The results highlight that robots are less accepted if they violate expectations by entering first or using a command. Interactions were more effective if participants expected an assertive robot which then asked politely for priority and entered first. The findings emphasize the importance of power expectations in human-robot conflicts for the robot's evaluation and effectiveness in everyday situations.

Login notifications intend to inform users about sign-ins and help them protect their accounts from unauthorized access. Notifications are usually sent if a login deviates from previous ones, potentially indicating malicious activity. They contain information like the location, date, time, and device used to sign in. Users are challenged to verify whether they recognize the login (because it was them or someone they know) or to protect their account from unwanted access. In a user study, we explore users' comprehension, reactions, and expectations of login notifications. We utilize two treatments to measure users' behavior in response to notifications sent for a login they initiated or based on a malicious actor relying on statistical sign-in information. We find that users identify legitimate logins but need more support to halt malicious sign-ins. We discuss the identified problems and give recommendations for service providers to ensure usable and secure logins for everyone.

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.

Personalized privacy assistants (PPAs) communicate privacy-related decisions of their users to Internet of Things (IoT) devices. There are different ways to implement PPAs by varying the degree of autonomy or decision model. This paper investigates user perceptions of PPA autonomy models and privacy profiles - archetypes of individual privacy needs - as a basis for PPA decisions in private environments (e.g., a friend's home). We first explore how privacy profiles can be assigned to users and propose an assignment method. Next, we investigate user perceptions in 18 usage scenarios with varying contexts, data types and number of decisions in a study with 1126 participants. We found considerable differences between the profiles in settings with few decisions. If the number of decisions gets high (> 1/h), participants exclusively preferred fully autonomous PPAs. Finally, we discuss implications and recommendations for designing scalable PPAs that serve as privacy interfaces for future IoT devices.

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.

While fitness trackers generate and present quantitative data, past research suggests that users often conceptualise their wellbeing in qualitative terms. This discrepancy between numeric data and personal wellbeing perception may limit the effectiveness of personal informatics tools in encouraging meaningful engagement with one’s wellbeing. In this work, we aim to bridge the gap between raw numeric metrics and users’ qualitative perceptions of wellbeing. In an online survey with $n=273$ participants, we used step data from fitness trackers and compared three presentation formats: standard charts, qualitative descriptions generated by an LLM (Large Language Model), and a combination of both. Our findings reveal that users experienced more reflection, focused attention and reward when presented with the generated qualitative data compared to the standard charts alone. Our work demonstrates how automatically generated data descriptions can effectively complement numeric fitness data, fostering a richer, more reflective engagement with personal wellbeing information.

Body ownership illusions (BOIs) occur when participants experience that their actual body is replaced by a body shown in virtual reality (VR). Based on a systematic review of the cumulative evidence on BOIs from 111 research articles published in 2010 to 2021, this article summarizes the findings of empirical studies of BOIs. Following the PRISMA guidelines, the review points to diverse experimental practices for inducing and measuring body ownership. The two major components of embodiment measurement, body ownership and agency, are examined. The embodiment of virtual avatars generally leads to modest body ownership and slightly higher agency. We also find that BOI research lacks statistical power and standardization across tasks, measurement instruments, and analysis approaches. Furthermore, the reviewed studies showed a lack of clarity in fundamental terminology, constructs, and theoretical underpinnings. These issues restrict scientific advances on the major components of BOIs, and together impede scientific rigor and theory-building.

Intergenerational communication can enhance well-being and family cohesion, but stereotypes and low empathy can be barriers to achieving effective communication. VR perspective-taking is a potential approach that is known to enhance understanding and empathy toward others by allowing a user to take another's viewpoint. In this study, we introduce LegacySphere, a novel VR perspective-taking experience leveraging the combination of embodiment, role-play, and storytelling. To explore LegacySphere's design and impact, we conducted an observational study involving five dyads with a one-generation gap. We found that LegacySphere promotes empathetic and reflexive intergenerational dialogue. Specifically, avatar embodiment encourages what we term "relationship cushioning,'' fostering a trustful, open environment for genuine communications. The blending of real and embodied identities prompts insightful questions, merging both perspectives. The experience also nurtures a sense of unity and stimulates reflections on aging. Our work highlights the potential of immersive technologies for enhancing empathetic intergenerational relationships.

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.

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.

Electrical Muscle Stimulation (EMS) has unique capabilities that can manipulate users' actions or perceptions, such as actuating user movement while walking, changing the perceived texture of food, and guiding movements for a user learning an instrument. These applications highlight the potential utility of EMS, but such benefits may be lost if users reject EMS. To investigate user acceptance of EMS, we conducted an online survey (N=101). We compared eight scenarios, six from HCI research applications and two from the sports and health domain. To gain further insights, we conducted in-depth interviews with a subset of the survey respondents (N=10). The results point to the challenges and potential of EMS regarding social and technological acceptance, showing that there is greater acceptance of applications that manipulate action than those that manipulate perception. The interviews revealed safety concerns and user expectations for the design and functionality of future EMS applications.

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

This work seeks to design and evaluate haptic feedback for sequential gestural inputs, where mid-air hand gestures are used to express system commands. Nine haptic patterns are first designed leveraging metaphors. To pursue efficient interaction, we examine the trade-off between pattern duration and recognition accuracy and find that durations as short as 0.3s-0.5s achieve roughly 80\%-90\% accuracy. We then examine the haptic design for sequential inputs, where we vary when the feedback for each gesture is provided, along with pattern duration, gesture sequence length, and age. Results show that providing haptic patterns right after detected hand gestures leads to significantly more efficient interaction compared with concatenating all haptic patterns after the gesture sequence. Moreover, the number of gestures had little impact on performance, but age is a significant predictor. Our results suggest that immediate feedback with 0.3s and 0.5s pattern duration would be recommended for younger and older users respectively.

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.

Rowing has great potential in Virtual Reality (VR) exergames as it requires physical effort and uses physical motion to map the locomotion in a virtual space. However, rowing in VR is currently restricted to locomotion along one axis, leaving 2D and 3D locomotion out of the scope. To facilitate rowing-based locomotion, we implemented three steering techniques based on head, hands, and feet movements for 2D and 3D VR environments. To investigate these methods, we conducted a controlled experiment (N = 24) to assess the user performance, experience and VR sickness. We found that head steering leads to fast and precise steering in 2D and 3D, and hand steering is the most realistic. Feet steering had the largest performance difference between 2D and 3D but comparable precision to hands in 2D. Lastly, head steering is the least mentally demanding, and all methods had comparable VR sickness.

The conflict between the rapid iteration demand of prototyping and the time-consuming nature of user tests has led researchers to adopt AI methods to identify usability issues. However, these AI-driven methods concentrate on evaluating the feasibility of a system, while often overlooking the influence of specified user characteristics and usage contexts. Our work proposes a tool named SimUser based on large language models (LLMs) with the Chain-of-Thought structure and user modeling method. It generates usability feedback by simulating the interaction between users and applications, which is influenced by user characteristics and contextual factors. The empirical study (48 human users and 21 designers) validated that in the context of a simple smartwatch interface, SimUser could generate heuristic usability feedback with the similarity varying from 35.7% to 100% according to the user groups and usability category. Our work provides insights into simulating users by LLM to improve future design activities.

Immersive Virtual Reality typically requires a head-mounted display (HMD) to visualize the environment and hand-held controllers to interact with the virtual objects. Recently, many applications display full-body avatars to represent the user and animate the arms to follow the controllers. Embodiment is higher when the self-avatar movements align correctly with the user. However, having a full-body self-avatar following the user's movements can be challenging due to the disparities between the virtual body and the user's body. This can lead to misalignments in the hand position that can be noticeable when interacting with virtual objects. In this work, we propose five different interaction modes to allow the user to interact with virtual objects despite the self-avatar and controller misalignment and study their influence on embodiment, proprioception, preference, and task performance. We modify aspects such as whether the virtual controllers are rendered, whether controllers are rendered in their real physical location or attached to the user's hand, and whether stretching the avatar arms to always reach the real controllers. We evaluate the interaction modes both quantitatively (performance metrics) and qualitatively (embodiment, proprioception, and user preference questionnaires). Our results show that the stretching arms solution, which provides body continuity and guarantees that the virtual hands or controllers are in the correct location, offers the best results in embodiment, user preference, proprioception, and performance. Also, rendering the controller does not have an effect on either embodiment or user preference.

We reflect on the design of a multispecies world centred around a bespoke enclosure in which three cats and a robot arm coexist for six hours a day during a twelve-day installation as part of an artist-led project. In this paper, we present the project's design process, encompassing various interconnected components, including the cats, the robot and its autonomous systems, the custom end-effectors and robot attachments, the diverse roles of the humans-in-the-loop, and the custom-designed enclosure. Subsequently, we provide a detailed account of key moments during the deployment and discuss the design implications for future multispecies systems. Specifically, we argue that designing the technology and its interactions is not sufficient, but that it is equally important to consider the design of the `world' in which the technology operates. Finally, we highlight the necessity of human involvement in areas such as breakdown recovery, animal welfare, and their role as audience.

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.

Generative AI applications present unique design challenges. As generative AI technologies are increasingly being incorporated into mainstream applications, there is an urgent need for guidance on how to design user experiences that foster effective and safe use. We present six principles for the design of generative AI applications that address unique characteristics of generative AI UX and offer new interpretations and extensions of known issues in the design of AI applications. Each principle is coupled with a set of design strategies for implementing that principle via UX capabilities or through the design process. The principles and strategies were developed through an iterative process involving literature review, feedback from design practitioners, validation against real-world generative AI applications, and incorporation into the design process of two generative AI applications. We anticipate the principles to usefully inform the design of generative AI applications by driving actionable design recommendations.

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.

About 20 percent of patients undergoing breast-conserving surgery require reoperation due to cancerous tissue remaining inside the breast. Breast cancer localization systems utilize auditory feedback to convey the distance between a localization probe and a small marker (seed) implanted into the breast tumor prior to surgery. However, no information on the location of the tumor margin is provided. To reduce the reoperation rate by improving the usability and accuracy of the surgical task, we developed an auditory display using shape sonification to assist with tumor margin localization. Accuracy and usability of the interactive shape sonification were determined on models of the female breast in three user studies with both breast surgeons and non-clinical participants. The comparative studies showed a significant increase in usability (p<0.05) and localization accuracy (p<0.001) of the shape sonification over the auditory feedback currently used in surgery.

Predicting users' emotional states during interaction is a long-standing goal of affective computing. However, traditional methods based on sensory data alone fall short due to the interplay between users' latent cognitive states and emotional responses. To address this, we introduce a computational cognitive model that simulates emotion as a continuous process, rather than a static state, during interactive episodes. This model integrates cognitive-emotional appraisal mechanisms with computational rationality, utilizing value predictions from reinforcement learning. Experiments with human participants demonstrate the model's ability to predict and explain the emergence of emotions such as happiness, boredom, and irritation during interactions. Our approach opens the possibility of designing interactive systems that adapt to users' emotional states, thereby improving user experience and engagement. This work also deepens our understanding of the potential of modeling the relationship between reward processing, reinforcement learning, goal-directed behavior, and appraisal.

During the grieving process, physical objects often serve as catalysts for remembering and honouring the relationship with departed loved ones. Leveraging a participatory design approach, we created Be.side, a fully customisable multi-modal artefact that incorporates scent, sound, and heartbeat stimulation and acts as a touch-point between the deceased and the bereaved. We conducted a four-week study with three participants to understand how the artefact, continuously attuned to each participant, helped to continue bonds with the deceased. Our results show that Be.side’s bespoke elements helped participants to evoke memories of the deceased. Participants created personalised rituals for remembrance. They sustained bonds by not only interacting with Be.side but also participating in the research. Finally, highlighting that remembrance can both provide comfort and deepen sadness, we discuss future design considerations.

We explore and evaluate single-handed folding interactions suitable for “modified clamshell flip phones” with a full screen touch display that folds in half along the short dimension. Three categories of interactions are identified: only-fold, touch-enhanced fold, and fold-enhanced touch; in which gestures are created using fold direction, fold magnitude, and touch position. A prototype evaluation device is built to resemble clamshell flip phones, but with a modified hinge and spring system to enable folding in both directions. A study investigates performance and preference for 30 fold gestures to discover which are most promising. To demonstrate how folding interactions could be incorporated into flip phone interfaces, applications such as map browsing, text editing, and menu shortcuts are described.

We present Stick&Slip, a novel approach that alters friction between the fingerpad & surfaces by depositing liquid droplets that coat the fingerpad. The liquid coating modifies the finger’s coefficient of friction, allowing users to feel surfaces up to ±60% more slippery or sticky. We selected our fluids to rapidly evaporate so that the surface returns to its original friction. Unlike traditional friction-feedback, such as electroadhesion or vibration, our approach: (1) alters friction on a wide range of surfaces and geometries, making it possible to modulate nearly any non-absorbent surface; (2) scales to many objects without requiring instrumenting the target surfaces (e.g., with conductive electrode coatings or vibromotors); and (3) both in/decreases friction via a single device. We identified nine liquids and characterized their practicality by measuring evaporation rates, etc. To illustrate the applicability of our approach, we demonstrate how it enables friction in virtual/mixed-reality or, even, while using everyday objects/tools.

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.

Modern software engineering is in a state of flux. With more development utilizing AI code generation tools and the continued reliance on online programming resources, understanding code and the original intent behind it is becoming more important than it ever has been. To this end, we have developed the "Meta-Manager'", a Visual Studio Code extension, with a supplementary browser extension, that automatically collects and organizes changes made to code while keeping track of the provenance of each part of the code, including code that has been AI-generated or copy-pasted from popular programming resources online. These sources and subsequent changes are represented in the editor and may be explored using searching and filtering mechanisms to help developers answer historically hard-to-answer questions about code, its provenance, and its design rationale. In our evaluation of Meta-Manager, we found developers were successfully able to use it to answer otherwise unanswerable questions about an unfamiliar code base.

Facial emotion recognition (FER) constitutes an essential social skill for both humans and machines to interact with others. To this end, computer interfaces serve as valuable tools for training individuals to improve FER abilities, while also serving as tools for gathering labels to train FER machine learning datasets. However, existing tools have limitations on the scope and methods of training non-clinical populations and also on collecting labels for machines. In this study, we introduce Find the Bot!, an integrated game that effectively engages the general population to support not only human FER learning on spontaneous expressions but also the collection of reliable judgment-based labels. We incorporated design guidelines from gamification, education, and crowdsourcing literature to engage and motivate players. Our evaluation (N=59) shows that the game encourages players to learn emotional social norms on perceived facial expressions with a high agreement rate, facilitating effective FER learning and reliable label collection all while enjoying gameplay.

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.

Charts are crucial in conveying information across various fields but are inaccessible to blind and low vision (BLV) people without assistive technology. Chart comprehension tools leveraging haptic feedback have been used widely but are often bulky, expensive, and static, rendering them inefficient for conveying chart data. To increase device portability, enable multitasking, and provide efficient assistance in chart comprehension, we introduce a novel system that delivers unobtrusive modulated electrotactile feedback directly to the fingertip edge. Our three-part study with twelve participants confirmed the effectiveness of this system, demonstrating that electrotactile feedback, when applied for 0.5 seconds with a 0.12-second interval, provides the most accurate position and direction recognition. Furthermore, our electrotactile device has proven valuable in assisting BLV participants in comprehending four commonly used charts: line charts, scatterplots, bar charts, and pie charts. We also delve into the implications of our findings on recognition enhancement, presentation modes, and function synergy.

'Juicy' or immediate abundant action feedback is widely held to make video games enjoyable and intrinsically motivating. Yet we do not know why it works: Which motives are mediating it? Which features afford it? In a pre-registered (n=1,699) online experiment, we tested three motives mapping prior practitioner discourse---effectance, competence, and curiosity---and connected design features. Using a dedicated action RPG and a 2x2+control design, we varied feedback amplification, success-dependence, and variability and recorded self-reported effectance, competence, curiosity, and enjoyment as well as free-choice playtime. Structural equation models show curiosity as the strongest enjoyment and only playtime predictor and support theorised competence pathways. Success dependence enhanced all motives, while amplification unexpectedly reduced them, possibly because the tested condition unintentionally impeded players' sense of agency. Our study evidences uncertain success affording curiosity as an underappreciated moment-to-moment engagement driver, directly supports competence-related theory, and suggests that prior juicy game feel guidance ties to legible action-outcome bindings and graded success as preconditions of positive 'low-level' user experience.

Rapid prototyping is an important tool for designers, but many fabrication techniques are slow and create bulky components requiring multiple machines and processes to achieve desired device shape and electronic functionality. Prior work explored ways to ease fabricating shapes or designing electronics, but we focus on creating shape and electrical pathways at the same time from a single material and machine. LaCir leverages a three-layered, laser-cuttable material to incorporate circuits into the structural substrate of the design using laser cutters. Our substrate features a layer of conductive material sandwiched between thermoplastic sheets, allowing designers to cut electrical traces and assembleable, 3D object geometry in a single pass. We evaluate different composite materials, weighing their cuttability, ease of assembly, and conductivity; we also show using fully laser-cut joints as structural and electrical connections. We demonstrate LaCir's flexibility through several example artifacts.