arXiv subjects

Aziz N Zeidieh

Publications and source records attributed to Aziz N Zeidieh.

3 recordsLinked to original sources

arXiv2026

Three Modalities, Two Design Probes, One Prototype, and No Vision: Experience-Based Co-Design of a Multi-modal 3D Data Visualization Tool

Three-dimensional (3D) data visualizations, such as surface plots, are vital in STEM fields from biomedical imaging to spectroscopy, yet remain largely inaccessible to blind and low-vision (BLV) people. To address this gap, we conducted an Experience-Based Co-Design with BLV co-designers with expertise in non-visual data representations to create an accessible, multi-modal, web-native visualization tool. Using a multi-phase methodology, our team of five BLV and one non-BLV researcher(s) participated in two iterative sessions, comparing a low-fidelity tactile probe with a high-fidelity digital prototype. This process produced a prototype with empirically grounded features, including reference sonification, stereo and volumetric audio, and configurable buffer aggregation, which our co-designers validated as improving analytic accuracy and learnability. In this study, we target core analytic tasks essential for non-visual 3D data exploration: orientation, landmark and peak finding, comparing local maxima versus global trends, gradient tracing, and identifying occluded or partially hidden features. Our work offers accessibility researchers and developers a co-design protocol for translating tactile knowledge to digital interfaces, concrete design guidance for future systems, and opportunities to extend accessible 3D visualization into embodied data environments.

cs.HC↗

arXiv2025

vashTimer: A Multi-Purpose, Multimodal Mobile App For Maintaining Passage of Time by means of Visual, Auditory, Speech, and Haptic Alerts

Effective time management during presentations is challenging, particularly for Blind and Low-Vision (BLV) individuals, as existing tools often lack accessibility and multimodal feedback. To address this gap, we developed vashTimer: a free, open-source, and accessible iOS application. This paper demonstrates the design and functionality of vashTimer, which provides presenters with a robust tool for temporal awareness. The application delivers highly customizable alerts across four distinct modalities: visual, auditory, speech, and haptic; and supports multiple configurable intervals within a single session. By offering a flexible and non-intrusive time management solution, vashTimer empowers presenters of all visual abilities. The implications of this work extend beyond public speaking to any professional, such as a clinical therapist, who requires discreet temporal cues, fostering greater independence and focus for a wide range of users. This demonstration serves as the foundation for a planned formal user evaluation.

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↗