arXiv subjects

Omar Khan

Publications and source records attributed to Omar Khan.

9 recordsLinked to original sources

arXiv2026

QUARTZ: Qualitative Understanding via Accessible Representation and Visualization

Qualitative data visualizations -- concept maps, network graphs, Sankey diagrams, and coding stripes -- are integral to research practice, yet remain entirely inaccessible to blind and low-vision (BLV) researchers. While visualization has seen advanced multimodal solutions for quantitative charts, qualitative visualizations, and their non-linear, semantically rich structures have received no attention. We present QUARTZ, a web-based system that provides screen-reader-accessible, multimodal representations of qualitative data visualizations. Using the Rapid Iterative Testing and Evaluation (RITE) method, we conducted a user study with 8 BLV participants who completed 12 tasks across four visualization types. Our findings expose accessibility barriers unique to qualitative visualizations -- non-linear navigation breakdowns and semantic comprehension gaps absent from quantitative chart research---and document how iterative co-design with BLV users resolved them. We contribute empirical evidence and design guidelines for an underexplored visualization domain, advancing the infrastructure BLV researchers need to participate independently in qualitative inquiry.

cs.HC↗

arXiv2026

"I Don't Want My Mental Health App To Give Me Mental Health Barriers": Unpacking The Need For Digital Mental Health Tracking Services With And For The Blind Community

Digital mental health (DMH) tracking services promise continuous, personalized support for well-being, but their design often assumes sighted users. For the blind community, this assumption produces a distinct pattern of exclusion: services whose accessibility cannot be evaluated without first paying for them, community features that exclude the users they purport to support, and interfaces that leave users digitally literate but functionally blocked. We report on an explanatory sequential mixed-methods study of blind users' experiences with DMH tracking services in the United States. In the first phase, 93 legally blind adults completed a survey about their usage patterns, adoption decisions, and data-agency preferences; in the second, 10 survey respondents participated in semi-structured interviews. We analyzed closed-ended responses using descriptive statistics and the Kruskal-Wallis test, and open-ended and interview data using inductive thematic analysis, interpreting findings through Norman and Skinner's eHealth Literacy framework. Participants identified mindfulness, sleep, and goal-tracking services as their most-used categories, but also described recurring exclusion from the community-support features that other users value most. We argue that the framework's "computer literacy" dimension is insufficient on its own: many of our participants possessed the literacy but were blocked from applying it by design choices that predate the user. We contribute design recommendations for transparent pre-purchase accessibility evaluation, accessibility-native rather than retrofitted interfaces, and user-controlled data agency -- recommendations intended not to accommodate blind users but to design DMH tracking services with them from the start.

cs.HC↗

arXiv2026

UMI: GPU-Accelerated Asymmetric Robust Estimator for Photometric Detrending in Exoplanet Transit Searches

We present UMI (Unified Median Iterative), a novel robust location estimator for detrending photometric time series in exoplanet transit surveys. UMI modifies the standard Tukey bisquare M-estimator with two innovations: (1) an asymmetric weight function that penalizes downward deviations (transit dips) more aggressively than upward ones, exploiting the physical constraint that transits are always below the stellar continuum, and (2) an upper-RMS scale estimator computed from above-median residuals only, ensuring that transit dips never contaminate the noise estimate. Implemented as a fused HIP/CUDA GPU kernel, UMI achieves 69x faster detrending (3.4 ms vs 234 ms per star) and 37x faster full pipeline throughput compared to the wotan biweight implementation. Injection-recovery tests across TESS, Kepler, and K2 show that UMI's advantage is concentrated at planet-scale transit depths above the photometric noise floor: at 0.1% transit depth, UMI reduces median depth recovery error from 20.5% to 15.8% on TESS (23% improvement) and from 14.6% to 4.2% on Kepler (71% improvement). At shallower depths approaching the noise floor, all sliding-window methods converge to comparable performance. Validated across 802 confirmed exoplanets from TESS and Kepler, UMI occupies a previously unfilled region of the speed-accuracy tradeoff for transit detrending. The tool is publicly available as pip install torchflat.

astro-ph.IM↗

arXiv2026

"I Don't Trust Any Professional Research Tool": A Re-Imagination of Knowledge Production Workflows by, with, and for Blind and Low-Vision Researchers

Research touts universal participation through accessibility initiatives, yet blind and low-vision (BLV) researchers face systematic exclusion as visual representations dominate modern research workflows. To materialize inclusive processes, we, as BLV researchers, examined how our peers combat inaccessible infrastructures. Through an explanatory sequential mixed-methods approach, we conducted a cross-sectional, observational survey (n=57) and follow-up semi-structured interviews (n=15), analyzing open-ended data using reflexive thematic analysis and framing findings through activity theory to highlight research's systemic shortcomings. We expose how BLV researchers sacrifice autonomy and shoulder physical burdens, with nearly one-fifth unable to independently perform literature review or evaluate visual outputs, delegating tasks to sighted colleagues or relying on AI-driven retrieval to circumvent fatigue. Researchers also voiced frustration with specialized tools, citing developers' performative responses and losing deserved professional accolades. We seek follow-through on research's promises through design recommendations that reconceptualize accessibility as fundamental to successful research and supporting BLV scholars' workflows.

cs.HC↗

arXiv2025

Sensing the Shape of Data: Non-Visual Exploration of Statistical Concepts in Histograms with Blind and Low-Vision Learners

Statistical concepts often rely heavily on visual cues for comprehension, presenting challenges for individuals who face difficulties using visual information, such as the blind and low-vision (BLV) community. While prior work has explored making data visualizations accessible, limited research examines how BLV individuals conceptualize and learn the underlying statistical concepts these visualizations represent. To better understand BLV individuals' learning strategies for potentially unfamiliar statistical concepts, we conducted a within-subjects experiment with 7 BLV individuals, controlling for vision condition using blindfolds. Each participant leveraged three different non-visual representations (Swell Touch tactile graph (STGs), shaped data patterns on a refreshable display (BDPs), sonification) to understand three different statistical concepts in histograms (skewness, modality, kurtosis). We collected quantitative metrics (accuracy, completion time, self-reported confidence levels) and qualitative insights (gesture analysis) to identify participants' unique meaning-making strategies. Results revealed that the braille condition led to the most accurate results, with sonification tasks being completed the fastest. Participants demonstrated various adaptive techniques when exploring each histogram, often developing alternative mental models that helped them non-visually encode statistical visualization concepts. Our findings reveal important implications for statistics educators and assistive technology designers, suggesting that effective learning tools must go beyond simple translation of visual information to support the unique cognitive strategies employed by BLV learners.

cs.HC↗

arXiv2025

NORA: A Nephrology-Oriented Representation Learning Approach Towards Chronic Kidney Disease Classification

Chronic Kidney Disease (CKD) affects millions of people worldwide, yet its early detection remains challenging, especially in outpatient settings where laboratory-based renal biomarkers are often unavailable. In this work, we investigate the predictive potential of routinely collected non-renal clinical variables for CKD classification, including sociodemographic factors, comorbid conditions, and urinalysis findings. We introduce the Nephrology-Oriented Representation leArning (NORA) approach, which combines supervised contrastive learning with a nonlinear Random Forest classifier. NORA first derives discriminative patient representations from tabular EHR data, which are then used for downstream CKD classification. We evaluated NORA on a clinic-based EHR dataset from Riverside Nephrology Physicians. Our results demonstrated that NORA improves class separability and overall classification performance, particularly enhancing the F1-score for early-stage CKD. Additionally, we assessed the generalizability of NORA on the UCI CKD dataset, demonstrating its effectiveness for CKD risk stratification across distinct patient cohorts.

cs.LG↗

arXiv2025

PunchPulse: A Physically Demanding Virtual Reality Boxing Game Designed with, for and by Blind and Low-Vision Players

Blind and low-vision (BLV) individuals experience lower levels of physical activity (PA) compared to sighted peers due to a lack of accessible, engaging exercise options. Existing solutions often rely on auditory cues but do not fully integrate rich sensory feedback or support spatial navigation, limiting their effectiveness. This study introduces PunchPulse, a virtual reality (VR) boxing exergame designed to motivate BLV users to reach and sustain moderate to vigorous physical activity (MVPA) levels. Over a seven-month, multi-phased study, PunchPulse was iteratively refined with three BLV co-designers, informed by two early pilot testers, and evaluated by six additional BLV user-study participants. Data collection included both qualitative (researcher observations, SOPI) and quantitative (MVPA zones, aid usage, completion times) measures of physical exertion and gameplay performance. The user study revealed that all participants reached moderate MVPA thresholds, with high levels of immersion and engagement observed. This work demonstrates the potential of VR as an inclusive medium for promoting meaningful PA in the BLV community and addresses a critical gap in accessible, intensity-driven exercise interventions.

cs.HC↗

arXiv2025

"Sighted People Have Their Pick Of The Litter": Unpacking The Need For Digital Mental Health (DMH) Tracking Services With And For The Blind Community

The proliferation of digital mental health (DMH) tracking services promises personalized support, yet accessibility barriers limit equal access. This study investigates blind community experiences with DMH tracking services across the United States as a step toward inclusive health technology design. Working with blind advocacy organizations, we distributed a cross-sectional observational survey (n = 93) and analyzed open-ended responses using Norman and Skinner's eHealth Literacy framework. Our findings reveal significant challenges in navigation, content interpretation, and overall user experience, which impede the blind community's effective engagement with DMH tools. Results highlight the need for adaptive interfaces, accessible tracking strategies, and voice-guided interactions. These insights inform design recommendations for developers and policymakers, promoting more inclusive mental health technologies. By prioritizing accessibility, we make forward progress in ensuring that DMH tracking services fulfill their potential to support mental well-being across diverse user groups, fostering digital equality in mental health care.

cs.HC↗

arXiv2019

Multisensory cues facilitate coordination of stepping movements with a virtual reality avatar

The effectiveness of simple sensory cues for retraining gait have been demonstrated, yet the feasibility of humanoid avatars for entrainment have yet to be investigated. Here, we describe the development of a novel method of visually cued training, in the form of a virtual partner, and investigate its ability to provide movement guidance in the form of stepping. Real stepping movements were mapped onto an avatar using motion capture data. The trajectory of one of the avatar step cycles was then accelerated or decelerated by 15% to create a perturbation. Healthy participants were motion captured while instructed to step in time to the avatar's movements, as viewed through a virtual reality headset. Step onset times were used to measure the timing errors (asynchronies) between them. Participants completed either a visual-only condition, or auditory-visual with footstep sounds included. Participants' asynchronies exhibited slow drift in the Visual-Only condition, but became stable in the Auditory-Visual condition. Moreover, we observed a clear corrective response to the phase perturbation in both auditory-visual conditions. We conclude that an avatar's movements can be used to influence a person's own gait, but should include relevant auditory cues congruent with the movement to ensure a suitable accuracy is achieved.

cs.HC↗