Three-dimensional (3D) data visualizations, such as surface plots, are vital in STEM fields from biomedical imaging to meteorology and spectroscopy, yet remain largely inaccessible to blind and low-vision (BLV) people. To address this gap, we conducted an Experience-Based Co-Design (EBCD) 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 co-design 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 BLV co-designers validated as improving analytic accuracy and learnability. In this study, we explicitly target core analytic tasks essential for non-visual 3D data exploration: 3D 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.

Blind and low-vision (BLV) individuals experience lower levels of physical activity (PA) due to limited access to engaging, accessible exercise tools.We present PunchPulse, an open-source VR boxing exergame (available on GitHub) designed in collaboration with BLV co-designers to support sustained moderate-to-vigorous physical activity (MVPA) through immersive, autonomous gameplay.Our system emphasizes structured progression and multimodal interaction (e.g., heart-rate tracking, audio-haptic feedback) to scaffold engagement and exertion.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, semi-structured interviews) 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.

Blind and low-vision (BLV) users remain largely excluded from three-dimensional (3D) surface and point data visualizations due to the reliance on visual interaction. Existing approaches inadequately support non-visual access, especially in browser-based environments. This study introduces DIXTRAL, a hosted web-native system, co-designed with BLV researchers to address these gaps through multimodal interaction. Conducted with two blind and one sighted researcher, this study took place over sustained design sessions. Data were gathered through iterative testing of the prototype, collecting feedback on spatial navigation, sonification, and usability. Co-design observations demonstrate that synchronized auditory, visual, and textual feedback, combined with keyboard and gamepad navigation, enhances both structure discovery and orientation. DIXTRAL aims to improve access to 3D continuous scalar fields for BLV users and inform best practices for creating inclusive 3D visualizations.

Virtual reality (VR) has been celebrated for its immersive experiences, yet its potential for creating accessible and enjoyable environments for Blind and Low-Vision (BLV) individuals remains underexplored. Our project addresses this gap by developing a VR table tennis game specifically designed for BLV players. Utilizing an autoethnographic approach, our mixed-ability team, including three BLV co-designers, prototyped the game through rapid iterative testing and evaluation over four months. We integrated multi-sensory feedback mechanisms, such as spatial audio, haptic feedback, and high-contrast visuals, to enhance navigation and interaction. Our findings highlight the effectiveness of combining these modalities to create an enjoyable and realistic VR sports experience. However, we also identified challenges, such as the need for balanced sensory feedback to avoid overload. This study emphasizes the importance of inclusive design in VR gaming, offering new recreational opportunities for BLV individuals and setting the stage for future advancements in accessible VR technology.

This paper investigates how blind and low-vision (BLV) users interact with multimodal large language models (LLMs) to interpret data visualizations. Building upon our previous work on the multimodal access and interactive data representation (MAIDR) framework, our mixed-visual-ability team co-designed maidrAI, an LLM extension providing multiple AI responses to users’ visual queries. To explore generative AI-based data representation, we conducted user studies with 8 BLV participants, tasking them with interpreting box plots using our system. We examined how participants personalize LLMs through prompt engineering, their preferences for data visualization descriptions, and strategies for verifying LLM responses. Our findings highlight three dimensions affecting BLV users’ decision-making process: modal preference, LLM customization, and multimodal data representation. This research contributes to designing more accessible data visualization tools for BLV users and advances the understanding of inclusive generative AI applications.