Social VR has increased in popularity due to its affordances for rich, embodied, and nonverbal communication. However, nonverbal communication remains inaccessible for blind and low vision people in social VR. We designed accessible cues with audio and haptics to represent three nonverbal behaviors: eye contact, head shaking, and head nodding. We evaluated these cues in real-time conversation tasks where 16 blind and low vision participants conversed with two other users in VR. We found that the cues were effective in supporting conversations in VR. Participants had statistically significantly higher scores for accuracy and confidence in detecting attention during conversations with the cues than without. We also found that participants had a range of preferences and uses for the cues, such as learning social norms. We present design implications for handling additional cues in the future, such as the challenges of incorporating AI. Through this work, we take a step towards making interpersonal embodied interactions in VR fully accessible for blind and low vision people.
While VR is increasingly used for social interactions, it continues to be inaccessible for blind and low vision users. During such interactions, VR platforms allow users to communicate with naturalistic nonverbal cues, such as gaze, gestures, and body movements. In this work, we explored audio and haptic representations of gaze with a blind co-designer. We uncovered various preferences and recommendations for gaze representation. For example, receiving gaze information in multiple modalities was preferred, but audio feedback should be kept at a low volume to remain unobtrusive to conversations. Our work contributes design considerations to make gaze accessible in VR.
As virtual reality (VR) technology becomes more pervasive, it continues to find multiple new uses beyond research laboratories. One of them is distance adult education -- the potential of VR to provide valuable education experiences is massive, despite the current barriers to its widespread application. Nevertheless, recent trends demonstrate clearly that VR is on the rise in education settings, and VR-only courses are becoming more popular across the globe. This trend will continue as more affordable VR solutions are released commercially, increasing the number of education institutions that benefit from the technology. No accessibility guidelines exist at present that are created specifically for the design, development, and use of VR hardware and software in distance education. The purpose of this workshop is to address this niche. It gathers researchers and practitioners who are interested in education and intend to work together to formulate a set of practical guidelines for the use of VR in distance adult education to make it accessible to a wider range of people.
In virtual environments, many social cues (e.g. gestures, eye contact, and proximity) are currently conveyed visually or auditorily. Indicating social cues in other modalities, such as haptic cues to complement visual or audio signals, will help to increase VR's accessibility and take advantage of the platform's inherent flexibility. However, accessibility implementations in social VR are often siloed by single sensory modalities. To broaden the accessibility of social virtual reality beyond replacing one sensory modality with another, we identified a subset of social cues and built tools to enhance them allowing users to switch between modalities to choose how these cues are represented. Because consumer VR uses primarily visual and auditory stimuli, we started with social cues that were not accessible for blind and low vision (BLV) and d/Deaf and hard of hearing (DHH) people, and expanded how they could be represented to accommodate a number of needs. We describe how these tools were designed around the principle of social cue switching, and a standard distribution method to amplify reach.
"Scene description" applications that describe visual content in a photo are useful daily tools for blind and low vision (BLV) people. Researchers have studied their use, but they have only explored those that leverage remote sighted assistants; little is known about applications that use AI to generate their descriptions. Thus, to investigate their use cases, we conducted a two-week diary study where 16 BLV participants used an AI-powered scene description application we designed. Through their diary entries and follow-up interviews, users shared their information goals and assessments of the visual descriptions they received. We analyzed the entries and found frequent use cases, such as identifying visual features of known objects, and surprising ones, such as avoiding contact with dangerous objects. We also found users scored the descriptions relatively low on average, 2.76 out of 5 (SD=1.49) for satisfaction and 2.43 out of 4 (SD=1.16) for trust, showing that descriptions still need significant improvements to deliver satisfying and trustworthy experiences. We discuss future opportunities for AI as it becomes a more powerful accessibility tool for BLV users.
The rapid growth of virtual reality (VR) has led to increased use of social VR platforms for interaction. However, these platforms lack adequate features to support blind and low vision (BLV) users, posing significant challenges in navigation, visual interpretation, and social interaction. One promising approach to these challenges is employing human guides in VR. However, this approach faces limitations with a lack of availability of humans to serve as guides, or the inability to customize the guidance a user receives from the human guide. We introduce an AI-powered guide to address these limitations. The AI guide features six personas, each offering unique behaviors and appearances to meet diverse user needs, along with visual interpretation and navigation assistance. We aim to use this AI guide in the future to help us understand BLV users' preferences for guide forms and functionalities.
"Scene description" applications that describe visual content in a photo are useful daily tools for blind and low vision (BLV) people. Researchers have studied their use, but they have only explored those that leverage remote sighted assistants; little is known about applications that use AI to generate their descriptions. Thus, to investigate their use cases, we conducted a two-week diary study where 16 BLV participants used an AI-powered scene description application we designed. Through their diary entries and follow-up interviews, users shared their information goals and assessments of the visual descriptions they received. We analyzed the entries and found frequent use cases, such as identifying visual features of known objects, and surprising ones, such as avoiding contact with dangerous objects. We also found users scored the descriptions relatively low on average, 2.76 out of 5 (SD=1.49) for satisfaction and 2.43 out of 4 (SD=1.16) for trust, showing that descriptions still need significant improvements to deliver satisfying and trustworthy experiences. We discuss future opportunities for AI as it becomes a more powerful accessibility tool for BLV users.
