I-VAMOS: Independent Voting with Accessible Multimodal Offline System for Visually Impaired UsersIndependent and secret voting is a constitutional right, yet blind and low-vision voters (BLVs) continue to face barriers when casting their votes. Existing methods such as tactile templates often require braille literacy or assistance, while electronic ballot-marking devices raise cost and security concerns. We present I-VAMOS, a voting assistance system that enables BLVs to cast paper ballots securely and independently. Based on participatory sessions with BLVs, I-VAMOS integrates a ballot slide frame, a spring-loaded stamp, and real-time OCR-based speech and visual feedback, operating offline without the need for customized templates. With the improved I-VAMOS, we conducted a user study (n=16), balanced across vision status, braille literacy, and age. Results showed that I-VAMOS significantly reduced workload (NASA–TLX; 26.1) and improved stamping accuracy (91.7%) and usability (SUS; 79.1) compared to existing aids, though took longer completion times (29.6s). These findings emphasize that I-VAMOS enables independent and confidential voting for BLVs.2026GKGyeongdeok Kim et al.Gwangju Institute of Science and TechnologyVisual Impairment Technologies (Screen Readers, Tactile Graphics, Braille)Universal & Inclusive DesignParticipatory DesignCHI
µCap: Instrumental Music Captions for Deaf and Hard-of-Hearing IndividualsInstrumental music conveys rich affective experiences through acoustic cues, yet instrumental passages often remain inaccessible to Deaf and Hard-of-Hearing (DHH) audiences. Although captioning practices for vocal songs have expanded, instrumental music remains largely uncaptioned, with no established criteria for representing musical content in text. We propose 𝜇Cap (Music Captions), an automatic instrumental music captioning system that transforms instrumental audio into time-aligned, non-lexical textual renderings enhanced with simple visuals. Drawing on Preliminary surveys with DHH individuals and expert group discussions, we developed a phonetic-like captioning schema grounded in music sound analysis and linguistics. We then implemented 𝜇Cap using audio feature extraction and a Retrieval-Augmented Generation(RAG) pipeline to produce expressive, sound-mimetic captions. Two user evaluations with DHH participants (n=20 and n=15) showed that 𝜇Cap enhanced music appreciation, immersion, and perceived presence of acoustic detail. This work contributes empirical evidence and insights for designing caption-based visual representations that make instrumental music more accessible.2026SASooYeon Ahn et al.Gwangju Institute of Science and TechnologyDeaf & Hard-of-Hearing Support (Captions, Sign Language, Vibration)Audio Accessibility (Captions, Sign Language, Vibration)CHI
When Fingers Become Tools: Rendering Virtual Tool Inertia with a Finger-Mounted Extending RodWe present the Finger-Mounted Extending Rod, a wearable device that transforms fingers into virtual tools by modulating fingertip mass distribution. We employ linear actuators on fingers that extend or retract metal rods according to their poses, generating rotational inertia while redirecting the hand to natural grip postures. Through three user studies, we evaluate (1) finger pose embodiment under visual redirection and tool matching via inertia tensor similarity, (2) perception of tool length and rotational inertia, and (3) VR tool interaction experience. Results show that 10 of 15 finger poses maintained embodiment, exhibiting inertia tensor similarities of 0.936–0.991 with their matched tools and yielding perceived inertia amplifications of 4.19–10.45×; moreover, aligning inertia tensors to virtual tools enhanced immersion, realism, and enjoyment compared to misaligned or no-device conditions across six VR scenarios. We conclude by discussing how the system renders virtual tools through the fingers and enhances their perception with inertia modulation.2026SKSeongjun Kang et al.Gwangju Institute of Science and TechnologyForce Feedback & Pseudo-Haptic WeightShape-Changing Interfaces & Soft Robotic MaterialsImmersion & Presence ResearchCHI
VisiPrint: Previewing 3D-Print Appearance from Real Material SamplesWe present VisiPrint, a tool for appearance-first previews of 3D-printed objects. Existing print preview slicers focus on toolpaths, not appearance, while pure rendering software is complex and cannot automatically reproduce slicing patterns. Prior work highlights persistent gaps between digital previews and printed results, such as color shifts, gloss/translucency changes, and layer-line highlights, motivating the creation of VisiPrint, an appearance-focused support tool. The VisiPrint algorithm combines slicer screenshots with filament photos via a custom diffusion-based synthesis pipeline. We present both a standalone user interface for VisiPrint compatible with any slicer and an Ultimaker Cura Plugin. We evaluate VisiPrint through a user study showing it is significantly faster, easier to use, and more faithful than alternatives: within a time-limit, participants completed 100% of preview tasks with VisiPrint, versus 63% with Cura and 13% with Blender. VisiPrint narrows the gap between design intent and printed appearance, complementing settings-centric tools with appearance-driven decision support.2026MPMaxine Perroni-Scharf et al.MIT CSAILDesktop 3D Printing & Personal FabricationCircuit Making & Hardware PrototypingCustomizable & Personalized ObjectsCHI
PREFAB: PREFerence-based Affective Modeling for Low-Budget Self-AnnotationSelf-annotation is the gold standard for collecting affective state labels in affective computing. Existing methods typically rely on full annotation, requiring users to continuously label affective states across entire sessions. While this process yields fine-grained data, it is time-consuming, cognitively demanding, and prone to fatigue and errors. To address these issues, we present PREFAB, a low-budget retrospective self-annotation method that targets affective inflection regions rather than full annotation. Grounded in the peak-end rule and ordinal representations of emotion, PREFAB employs a preference learning model to detect relative affective changes, directing annotators to label only selected segments while interpolating the remainder of the stimulus. We further introduce a preview mechanism that provides brief contextual cues to assist annotation. We evaluate PREFAB through a technical performance study and a 25-participant user study. Results show that PREFAB outperforms baselines in modeling affective inflections while mitigating workload (and conditionally mitigating temporal burden). Importantly, PREFAB improves annotator confidence without degrading annotation quality.2026JMJaeYoung Moon et al.GISTEmotion Recognition & DetectionAffective Feedback & Emotion Regulation InterfacesUser Research Methods (Interviews, Surveys, Observation)CHI
From Daily Song to Daily Self: Supporting Emotional Growth of Deaf and Hard-of-Hearing Individuals through Generative AI SongwritingThe rapid advancement of generative AI (GenAI) is expanding access to songwriting, offering a new medium of self-expression for Deaf and Hard-of-Hearing (DHH) individuals. However, emerging technologies that support DHH individuals in expressing themselves through music have largely been evaluated in single-session settings and often fall short in helping users unfamiliar with songwriting convey personal narratives or sustain engagement over time. This paper explores songwriting as an extended, music-based journaling practice that supports sustained emotional reflection over multiple sessions. We introduce SoulNote, a GenAI system enabling DHH to engage in iterative songwriting. Grounded in user-centered design, including a design workshop, a preliminary study, and a multi-session diary study, our findings show that ongoing songwriting with SoulNote facilitated emotional growth across three dimensions: self-insight, emotion regulation, and everyday attitudes toward emotions and self-care. Overall, this work demonstrates how GenAI can support marginalized communities by transforming creative expression into a daily practice of self-discovery and reflection.2026YCYoujin Choi et al.Gwangju Institute of Science and TechnologyGenerative AI (Text, Image, Music, Video)Affective Feedback & Emotion Regulation InterfacesAffective Human-Computer DialogueCHI
Understanding Gaze-Based Identification in VR Through Preattentive Processing and Binocular RivalryStimulus-evoked gaze dynamics offer a secure and hands-free signal in virtual reality (VR), yet the underlying design space of effective visual stimuli remains poorly understood. This work examines how preattentive processing and binocular rivalry can inform stimulus design for gaze-based identification in VR. We conducted a two-part study: (1) a feasibility assessment of closed-set identification performance with 26 participants and 44,928 gaze samples collected by using a commercial headset (Meta Quest Pro), and (2) a usability study with 16 participants comparing the same interaction in a login context to PIN and out-of-band methods as a potential authentication technique. Our findings confirm the feasibility of personal identification, highlight usability advantages, and reveal participants’ desire for greater transparency to understand individual variations in login results. Together, these results offer conceptual insights into the perceptual mechanisms shaping stimulus-evoked gaze behavior, and outline design implications for future VR authentication workflows.2026JJJunryeol Jeon et al.Gwangju Institute of Science and TechnologyEye Tracking & Gaze InteractionVoice User Interface (VUI) DesignPasswords & AuthenticationCHI
From Disruption to Immersion: Reimagining Vehicle Motion as Environmental Feedback through Force Mappings in In-Car VRThis study investigates how vehicle motion can be reinterpreted as perceptually coherent multisensory feedback for in-car VR applications, expanding beyond traditional motion-based experiences. We introduce the concept of force mappings, a design space that translates vehicle-induced physical forces such as from accelerations, turns, and rough terrain into ambient environmental representations within VR. Implemented on a real vehicle platform with a sensor-based pipeline, our system applies four representative mapping strategies (Ground-based, Wind-based, Current-based, Object-based) and evaluates their perceptual coherence and experiential effects through two respective user studies. Results show that force mappings improve presence, comfort, and engagement while enabling creative reinterpretations of physical motion. Finally, we provide empirical findings and design considerations to inform future in-car VR systems that leverage real-world motion as a creative and perceptually grounded interaction resource.2026BGBocheon Gim et al.Gwangju Institute of Science and TechnologyMotion Sickness & Passenger ExperienceSocial & Collaborative VRImmersion & Presence ResearchCHI
Designing a Generative AI-Assisted Music Psychotherapy Tool for Deaf and Hard-of-Hearing IndividualsSongwriting has long served as a powerful medium for expressing unconscious emotions and fostering self-awareness in psychotherapy. Due to the auditory-centric nature of traditional approaches, Deaf and Hard-of-Hearing (DHH) individuals have often been excluded from music’s therapeutic benefits. In response, this study presents a music psychotherapy tool co-designed with therapists, integrating conversational agents (CAs) and music generative AI as symbolic and therapeutic media. Through a usage study with 23 DHH individuals, we found that collaborative songwriting with the CA enabled them to experience emotional release, re-interpretation, and deeper self-understanding. In particular, the CA’s strategies—supportive empathy, example response options, and visual-based metaphors—were found to facilitate musical dialogue effectively for DHH individuals. These findings contribute to inclusive AI design by showing the potential of human–AI collaboration to bridge therapeutic and artistic practices.2026YCYoujin Choi et al.Gwangju Institute of Science and TechnologyGenerative AI (Text, Image, Music, Video)Intelligent Voice Assistants (Alexa, Siri, etc.)Deaf & Hard-of-Hearing Support (Captions, Sign Language, Vibration)CHI
HumanoidTurk: Expanding VR Haptics with Humanoids for Driving SimulationsWe explore how humanoid robots can be repurposed as haptic media, extending beyond their conventional role as social, assistive, collaborative agents. To illustrate this approach, we implemented HumanoidTurk, taking a first step toward a humanoid-based haptic system that translates in-game g-force signals into synchronized motion feedback in VR driving. A pilot study involving six participants compared two synthesis methods, leading us to adopt a filter-based approach for smoother and more realistic feedback. A subsequent study with sixteen participants evaluated four conditions: no-feedback, controller, humanoid+controller, and human+controller. Results showed that humanoid feedback enhanced immersion, realism, and enjoyment, while introducing moderate costs in terms of comfort and simulation sickness. Interviews further highlighted the robot’s consistency and predictability in contrast to the adaptability of human feedback. From these findings, we identify fidelity, adaptability, and versatility as emerging themes, positioning humanoids as a distinct haptic modality for immersive VR.2026DLDaeHo Lee et al.Gwangju Institute of Science and TechnologyIn-Vehicle Haptic, Audio & Multimodal FeedbackImmersion & Presence ResearchRobots in Education & HealthcareCHI
Guaranteeing Equitable Musical Collaboration: Lessons Learned from the Music-Making Activities in Mixed-Hearing GroupsIntegrating mixed-hearing groups in musical collaboration presents unique challenges and opportunities for their communication and equal contribution. This observational study aims to explore their collaborative work, focusing on the way for equitable music-making. We observed two music-making workshops to identify the potential and dynamics of their musical collaboration. While the first workshop proceeded in a traditional manner of music-making, the second workshop used an assistive tool with multimodality. Our findings highlight the dynamics in musical collaboration that foster engagement and bridge interaction gaps. In turn, sensory inclusion with multimodal music-making promoted role transition in mixed-hearing groups and their equal contributions, leading to the embracing of diverse cultural perspectives. Based on the insights derived from the observations, we propose a design guideline and future research directions for harnessing group dynamics and building equitable musical collaborations for an inclusive environment for mixed-hearing groups.2025CLChungHa Lee et al.Deaf and Hard-of-Hearing ResearchCSCW
AttraCar: Multisensory In-Car VR with Thermal, Airflow, and Motion Feedback through Built-In Vehicle SystemsWe introduce AttraCar, a novel multisensory in-car Virtual Reality (VR) platform that delivers thermal, airflow, and motion feedback using built-in vehicle systems. Leveraging the Heating, Ventilation, and Air Conditioning (HVAC) system for airflow and thermal variation, and the power seat for motion feedback, perceptual thresholds were determined through Just Noticeable Difference (JND) experiments. A user study evaluated six feedback conditions (Baseline, Ambient Airflow, Thermal Airflow, Seat Motion, Ambient Airflow + Seat Motion, Thermal Airflow + Seat Motion) during on-road VR scenarios. A subsequent on-road study demonstrates that different combinations of feedback are not only perceptually distinct but also highly effective in a dynamic VR context, significantly mitigating motion sickness and enhancing presence and haptic experience. We conclude with reflections on design considerations, integration challenges, and real-world applicability for scalable multisensory in-car VR systems utilizing existing vehicle components.2025DYAhmed Elsharkawy et al.In-Vehicle Haptic, Audio & Multimodal FeedbackMotion Sickness & Passenger ExperienceUIST
LegisFlow: Enhancing Korean Legal Research with Temporal-Aware LLM InterfacesIn South Korea's statutory law system, legal research faces challenges like tracking frequent amendments and understanding complex statute relationships. LegisFlow, an innovative AI-powered system, tackles these issues with features such as interactive amendment timelines and advanced inter-statute relationship analysis. Developed based on insights from Korean legal experts, it provides intuitive visualizations and context-aware search capabilities. A user study with 10 legal professionals demonstrated that LegisFlow significantly enhances efficiency, reducing task completion times by up to 36% (e.g., 440s vs. 690s in inter-statute comparison, p=0.022) and lowering cognitive load, with workflows streamlined by 70% fewer manual steps. LegisFlow transforms statutory law research by setting a new standard for AI-assisted tools, providing a scalable, user-centered solution for professionals in Korea and beyond.2025JKJunghwan Kim et al.Human-LLM CollaborationInteractive Data VisualizationUIST
EarPressure VR: Ear Canal Pressure Feedback for Enhancing Environmental Presence in Virtual RealityThis study presents EarPressure VR, a system that modulates ear canal pressure to simulate atmospheric pressure changes in virtual reality (VR). EarPressure VR employs sealed earbuds and a linear stepper motor–driven syringe to generate controlled pressure variations within safe limits (±40 hPa relative to ambient pressure). Through two user studies, we evaluate (1) perceptual thresholds for detecting ear pressure in terms of direction (inward or outward) and intensity differences, and (2) the effect of ear pressure feedback on users’ sense of environmental presence across two VR scenarios involving gradual and discrete changes in ambient pressure. Results show that participants reliably identified pressure direction at thresholds of +14.4 hPa (inward) and –23.8 hPa (outward), and intensity differences at ±14.6% and ±34.9%, respectively. Pressure feedback significantly improved presence ratings, particularly when pressure variation was continuously adjusted to reflect environmental transitions. We conclude by discussing the broader applicability of ear canal pressure feedback in areas such as training, simulation, and everyday experiences.2025SKSeongjun Kang et al.Mid-Air Haptics (Ultrasonic)Immersion & Presence ResearchUIST
EI-Lite: Electrical Impedance Sensing for Micro-gesture Recognition and Pinch Force EstimationMicro-gesture recognition and fine-grain pinch press enables intuitive and discreet control of devices, offering significant potential for enhancing human-computer interaction (HCI). In this paper, we present EI-Lite, a lightweight wrist-worn electrical impedance sensing device for micro-gesture recognition and continuous pinch force estimation. We elicit an optimal and simplified device architecture through an ablation study on electrode placement with 13 users, and implement the elicited designs through 3D printing. We capture data on 15 participants on (1) six common micro-gestures (plus idle state) and (2) index finger pinch forces, then develop machine learning models that interpret the impedance signals generated by these micro-gestures and pinch forces. Our system is capable of accurate recognition of micro-gesture events (96.33% accuracy), as well as continuously estimating the pinch force of the index finger in physical units (Newton), with the mean-squared-error (MSE) of 0.3071 (or mean-force-variance of 0.55 Newtons) over 15 participants. Finally, we demonstrate EI-Lite's applicability via three applications in AR/VR, gaming, and assistive technologies.2025JZJunyi Zhu et al.Vibrotactile Feedback & Skin StimulationFoot & Wrist InteractionUIST
BandEI: A Flexible Electrical Impedance Sensing Bandage for Deep Muscles and TendonsMonitoring deep muscles and tissues is critical for rehabilitation, training, and fine motor control. In this work, we propose BandEI, a flexible, bandage-like wearable sensor for electrical impedance sensing. BandEI utilizes woven conductive fabric as the core material for its electrodes and leverages digital fabrication, including laser cutting, to enable scalable and customizable fabrication. To streamline the design process, we provide a user interface that allows users to freely select the deployment location of BandEI. The interface automatically generates fabrication-ready design files that accommodate for the curvature and shape of the selected area. We evaluate BandEI and validate its ability to detect signals from actively engaged large muscles, such as the biceps and triceps. Additionally, it can capture signals from deep or passively activated muscles, like those in the hand, which are typically difficult to detect with conventional surface electromyography (sEMG). We design and implement BandEI for muscles in the fingers, neck, and ankle, demonstrating its capability for diverse applications, including real-time gesture recognition, neck motion monitoring, and gait tracking.2025HWHongrui Wu et al.Vibrotactile Feedback & Skin StimulationHaptic WearablesHuman Pose & Activity RecognitionUIST
Meta-antenna: Mechanically Frequency Reconfigurable Metamaterial AntennasWe introduce Meta-antenna, a design and fabrication pipeline for creating frequency reconfigurable antennas while making use of a single type of mechanical metamaterial structure. Unlike traditional static antenna systems with fixed radiation patterns and frequency responses per geometry, Meta-antenna leverages mechanical reconfiguration to alter the radiation and geometry characteristics of the antenna, making it more versatile for sensing and communication. Meta-antenna provides a design space of resonance frequency from 500 MHz to 6.3 GHz ≥10 dB upon the structure's compression, bending, or rotation. Additionally, we provide an Ansys-based editor that allows users to generate metamaterial antenna geometries and simulate their resonance frequency. We also provide a code template for Meta-antenna based sensing interactions. Our technical evaluation demonstrates that our fabricated Meta-antenna structures remain functional even after 10,000 compression cycles. Finally, we contribute three example applications showcasing Meta-antenna’s potential in adaptive personal devices, smart home systems, and tangible user interfaces.2025MAMarwa AlAlawi et al.Circuit Making & Hardware PrototypingCustomizable & Personalized ObjectsUIST
I Want to Break Free: Enabling User-Applied Active Locomotion in In-Car VR through Contextual CuesWe explore the feasibility of active user-applied locomotion in virtual reality (VR) within in-car environments, diverging from previous in-car VR research that synchronized virtual motion with the car's movement. Through a two-step study, we examined the effects of locomotion methods on user experience in dynamic vehicle environments and evaluated contextual cues designed to mitigate sensory mismatch caused by vehicle motion. The first study evaluated five locomotion methods, identifying joystick-based navigation as the most suitable for in-car use due to its low physical demand and stability. The second study focused on designing and testing contextual cues that translate physical sensations of vehicle motion into virtual effects without limiting the user’s freedom of movement, with results demonstrating their effectiveness in reducing motion sickness and enhancing presence. We conclude with initial insights and design considerations for expanding upon our findings in regards to enabling active locomotion in in-car VR.2025BGBocheon Gim et al.Gwangju Institute of Science and Technology, Human-Centered Intelligent Systems LabMotion Sickness & Passenger ExperienceSocial & Collaborative VRImmersion & Presence ResearchCHI
OnomaCap: Making Non-speech Sound Captions Accessible and Enjoyable through Onomatopoeic Sound RepresentationNon-speech sounds play an important role in setting the mood of a video and aiding comprehension. However, current non-speech sound captioning practices focus primarily on sound categories, which fails to provide a rich sound experience for d/Deaf and hard-of-hearing (DHH) viewers. Onomatopoeia, which succinctly captures expressive sound information, offers a potential solution but remains underutilized in non-speech sound captioning. This paper investigates how onomatopoeia benefits DHH audiences in non-speech sound captioning. We collected 7,962 sound-onomatopoeia pairs from listeners and developed a sound-onomatopoeia model that automatically transcribes sounds into onomatopoeic descriptions indistinguishable from human-generated ones. A user evaluation of 25 DHH participants using the model-generated onomatopoeia demonstrated that onomatopoeia significantly improved their video viewing experience. Participants most favored captions with onomatopoeia and category, and expressed a desire to see such captions across genres. We discuss the benefits and challenges of using onomatopoeia in non-speech sound captions, offering insights for future practices.2025JKJooYeong Kim et al.Gwangju Institute of Science and Technology, School of Integrated Technology/Soft Computing & Interaction LaboratoryVoice AccessibilityDeaf & Hard-of-Hearing Support (Captions, Sign Language, Vibration)Universal & Inclusive DesignCHI
MVPrompt: Building Music-Visual Prompts for AI Artists to Craft Music Video Mise-en-scèneMusic videos have traditionally been the domain of experts, but with text-to-video generative AI models, AI artists can now create them more easily. However, accurately reflecting the desired music-visual mise-en-scène remains challenging without specialized knowledge, highlighting the need for supportive tools. To address this, we conducted a design workshop with seven music video experts, identified design goals, and developed MVPrompt—a tool for generating music-visual mise-en-scène prompts. In a user study with 24 AI artists, MVPrompt outperformed the Baseline, effectively supporting the collaborative creative process. Specifically, the Visual Theme stage facilitated the exploration of tone and manner, while the Visual Scene & Grammar stage refined prompts with detailed mise-en-scène elements. By enabling AI artists to specify mise-en-scène creatively, MVPrompt enhances the experience of making music video scenes with text-to-video generative AI.2025CLChungHa Lee et al.Gwangju Institute of Science and Technology, School of Integrated Technology/Soft Computing & Interaction LaboratoryGenerative AI (Text, Image, Music, Video)AI-Assisted Creative WritingVideo Production & EditingCHI