arXiv subjects

Ruijia Chen

Publications and source records attributed to Ruijia Chen.

10 recordsLinked to original sources

arXiv2026

What to Distinguish and How? Opportunities and Challenges of Augmenting Multiple, Cluttered Objects in Complex Scenes for People with Low Vision

People with low vision (PLV) struggle to perceive complex scenes like busy kitchens and crowded streets, which contain many objects, visual clutter, and dynamic elements. Prior AR systems for low vision either enhance low-level visual features or augment task-relevant objects for single tasks in simple settings, leaving multi-object augmentation in complex scenes underexplored. Informed by a formative study characterizing important objects and their perceived importance for PLV, we built SceneGlance, a wearable AR system that recognizes important objects and visually distinguishes them by importance level. Through a controlled lab study with 12 PLV in a mock-up kitchen scene and a free-form think-aloud study with 13 PLV navigating an outdoor route, we found that AR distinction on object importance shifted PLV's attention toward objects of higher importance, and supported perception strategies such as building mental snapshots from the augmentation distribution and hierarchical scanning by importance. However, this attention shift came with a tradeoff of reduced overall scene recall. The studies also surfaced challenges posed by AR augmentations in complex scenes, such as adjacent augmentations blending or interfering with each other, yielding design implications for more practical AR vision enhancement systems in the complex real world.

cs.HC↗

arXiv2026

Head, Gaze, or Finger? Comparing Object Selection Techniques in Augmented Reality for People with Low Vision

Augmented reality (AR) can enhance visual perception for people with low vision (PLV) by overlaying multimodal information. Selection-based augmentation further allows users to flexibly choose and augment relevant information while reducing distraction and visual clutter. However, little is known about the ability and preferences of PLV in performing object selection techniques in AR, considering their potential visual and gaze control challenges. To understand what selection techniques are suitable for PLV to support selection-based AR augmentations, we conducted a mixed-methods study with 20 PLV and 18 sighted controls who performed target selection tasks using three input techniques -- head, gaze, and finger pointing with dwell-based confirmation -- in two real-world scenarios (sitting vs. on the go). We found that for PLV, gaze-based selection enabled the fastest initial pointing when sitting and comparable overall selection time to head-based selection in both scenarios; however, due to reduced gaze stability, head-based selection remained the most stable and the least mentally demanding. Uniquely, participants with central vision loss preferred finger-based selection, reporting a greater sense of control. Our results provide empirical insights into accessible AR interaction techniques and selection-based vision enhancements for PLV.

cs.HC↗

arXiv2026

NaviNote: Enabling In-situ Spatial Annotation Authoring to Support Exploration and Navigation for Blind and Low Vision People

GPS and smartphones enable users to place location-based annotations, capturing rich environmental context. Previous research demonstrates that blind and low vision (BLV) people can use annotations to explore unfamiliar areas. However, current commercial systems allowing BLV users to create annotations have never been evaluated, and current GPS-based systems can deviate several meters. Motivated by high-accuracy visual positioning technology, we first conducted a formative study with 24 BLV participants to envision a more accurate and inclusive annotation system. Surprisingly, many participants viewed the high-accuracy technology not just as an annotation system but also as a tool for precise last-few-meters navigation. Guided by participant feedback, we developed NaviNote, which combines vision-based high-precision localization with an agentic architecture to enable voice-based annotation authoring and navigation. Evaluating NaviNote with 18 BLV participants showed that it significantly improved navigation performance and supported users in understanding and annotating their surroundings. Based on these findings, we discuss design considerations for future accessible annotation authoring systems.

cs.HC↗

arXiv2025

FocusView: Understanding and Customizing Informational Video Watching Experiences for Viewers with ADHD

While videos have become increasingly prevalent in delivering information across different educational and professional contexts, individuals with ADHD often face attention challenges when watching informational videos due to the dynamic, multimodal, yet potentially distracting video elements. To understand and address this critical challenge, we designed FocusView, a video customization interface that allows viewers with ADHD to customize informational videos from different aspects. We evaluated FocusView with 12 participants with ADHD and found that FocusView significantly improved the viewability of videos by reducing distractions. Through the study, we uncovered participants' diverse perceptions of video distractions (e.g., background music as a distraction vs. stimulation boost) and their customization preferences, highlighting unique ADHD-relevant needs in designing video customization interfaces (e.g., reducing the number of options to avoid distraction caused by customization itself). We further derived design considerations for future video customization systems for the ADHD community.

cs.HC↗

arXiv2025

FocusView: Understanding and Customizing Informational Video Watching Experiences for Viewers with ADHD

While videos have become increasingly prevalent in delivering information across different educational and professional contexts, individuals with ADHD often face attention challenges when watching informational videos due to the dynamic, multimodal, yet potentially distracting video elements. To understand and address this critical challenge, we designed \textit{FocusView}, a video customization interface that allows viewers with ADHD to customize informational videos from different aspects. We evaluated FocusView with 12 participants with ADHD and found that FocusView significantly improved the viewability of videos by reducing distractions. Through the study, we uncovered participants' diverse perceptions of video distractions (e.g., background music as a distraction vs. stimulation boost) and their customization preferences, highlighting unique ADHD-relevant needs in designing video customization interfaces (e.g., reducing the number of options to avoid distraction caused by customization itself). We further derived design considerations for future video customization systems for the ADHD community.

cs.HC↗

arXiv2025

Modeling the Impact of Visual Stimuli on Redirection Noticeability with Gaze Behavior in Virtual Reality

While users could embody virtual avatars that mirror their physical movements in Virtual Reality, these avatars' motions can be redirected to enable novel interactions. Excessive redirection, however, could break the user's sense of embodiment due to perceptual conflicts between vision and proprioception. While prior work focused on avatar-related factors influencing the noticeability of redirection, we investigate how the visual stimuli in the surrounding virtual environment affect user behavior and, in turn, the noticeability of redirection. Given the wide variety of different types of visual stimuli and their tendency to elicit varying individual reactions, we propose to use users' gaze behavior as an indicator of their response to the stimuli and model the noticeability of redirection. We conducted two user studies to collect users' gaze behavior and noticeability, investigating the relationship between them and identifying the most effective gaze behavior features for predicting noticeability. Based on the data, we developed a regression model that takes users' gaze behavior as input and outputs the noticeability of redirection. We then conducted an evaluation study to test our model on unseen visual stimuli, achieving an accuracy of 0.012 MSE. We further implemented an adaptive redirection technique and conducted a proof-of-concept study to evaluate its effectiveness with complex visual stimuli in two applications. The results indicated that participants experienced less physical demanding and a stronger sense of body ownership when using our adaptive technique, demonstrating the potential of our model to support real-world use cases.

cs.HC↗

arXiv2025

VisiMark: Characterizing and Augmenting Landmarks for People with Low Vision in Augmented Reality to Support Indoor Navigation

Landmarks are critical in navigation, supporting self-orientation and mental model development. Similar to sighted people, people with low vision (PLV) frequently look for landmarks via visual cues but face difficulties identifying some important landmarks due to vision loss. We first conducted a formative study with six PLV to characterize their challenges and strategies in landmark selection, identifying their unique landmark categories (e.g., area silhouettes, accessibility-related objects) and preferred landmark augmentations. We then designed VisiMark, an AR interface that supports landmark perception for PLV by providing both overviews of space structures and in-situ landmark augmentations. We evaluated VisiMark with 16 PLV and found that VisiMark enabled PLV to perceive landmarks they preferred but could not easily perceive before, and changed PLV's landmark selection from only visually-salient objects to cognitive landmarks that are more important and meaningful. We further derive design considerations for AR-based landmark augmentation systems for PLV.

cs.HC↗

arXiv2025

Characterizing Visual Intents for People with Low Vision through Eye Tracking

Accessing visual information is crucial yet challenging for people with low vision due to visual conditions like low visual acuity and limited visual fields. However, unlike blind people, low vision people have and prefer using their functional vision in daily tasks. Gaze patterns thus become an important indicator to uncover their visual challenges and intents, inspiring more adaptive visual support. We seek to deeply understand low vision users' gaze behaviors in different image-viewing tasks, characterizing typical visual intents and the unique gaze patterns exhibited by people with different low vision conditions. We conducted a retrospective think-aloud study using eye tracking with 20 low vision participants and 20 sighted controls. Participants completed various image-viewing tasks and watched the playback of their gaze trajectories to reflect on their visual experiences. Based on the study, we derived a visual intent taxonomy with five visual intents characterized by participants' gaze behaviors. We demonstrated the difference between low vision and sighted participants' gaze behaviors and how visual ability affected low vision participants' gaze patterns across visual intents. Our findings underscore the importance of combining visual ability information, visual context, and eye tracking data in visual intent recognition, setting up a foundation for intent-aware assistive technologies for low vision people.

cs.HC↗

arXiv2024

Exploring the Design Space of Optical See-through AR Head-Mounted Displays to Support First Responders in the Field

First responders (FRs) navigate hazardous, unfamiliar environments in the field (e.g., mass-casualty incidents), making life-changing decisions in a split second. AR head-mounted displays (HMDs) have shown promise in supporting them due to its capability of recognizing and augmenting the challenging environments in a hands-free manner. However, the design space have not been thoroughly explored by involving various FRs who serve different roles (e.g., firefighters, law enforcement) but collaborate closely in the field. We interviewed 26 first responders in the field who experienced a state-of-the-art optical-see-through AR HMD, as well as its interaction techniques and four types of AR cues (i.e., overview cues, directional cues, highlighting cues, and labeling cues), soliciting their first-hand experiences, design ideas, and concerns. Our study revealed both generic and role-specific preferences and needs for AR hardware, interactions, and feedback, as well as identifying desired AR designs tailored to urgent, risky scenarios (e.g., affordance augmentation to facilitate fast and safe action). While acknowledging the value of AR HMDs, concerns were also raised around trust, privacy, and proper integration with other equipment. Finally, we derived comprehensive and actionable design guidelines to inform future AR systems for in-field FRs.

cs.HC↗

arXiv2022

Modeling the Noticeability of User-Avatar Movement Inconsistency for Sense of Body Ownership Intervention

An avatar mirroring the user's movement is commonly adopted in Virtual Reality(VR). Maintaining the user-avatar movement consistency provides the user a sense of body ownership and thus an immersive experience. However, breaking this consistency can enable new interaction functionalities, such as pseudo haptic feedback or input augmentation, at the expense of immersion. We propose to quantify the probability of users noticing the movement inconsistency while the inconsistency amplitude is being enlarged, which aims to guide the intervention of the users' sense of body ownership in VR. We applied angular offsets to the avatar's shoulder and elbow joints and recorded whether the user identified the inconsistency through a series of three user studies and built a statistical model based on the results. Results show that the noticeability of movement inconsistency increases roughly quadratically with the enlargement of offsets and the offsets at two joints negatively affect the probability distributions of each other. Leveraging the model, we implemented a technique that amplifies the user's arm movements with unnoticeable offsets and then evaluated implementations with different parameters(offset strength, offset distribution). Results show that the technique with medium-level and balanced-distributed offsets achieves the best overall performance. Finally, we demonstrated our model's extendability in interventions in the sense of body ownership with three VR applications including stroke rehabilitation, action game and widget arrangement.

cs.HC↗