Vision-Based Assistive Technologies for People with Cerebral Visual Impairment: A Review and Focus Study

2024-10-27

International ACM SIGACCESS Conference on Computers and Accessibility

AI Summary

Existing vision-based assistive technologies (VBATs) predominantly address ocular visual impairments, largely neglecting cerebral visual impairment (CVI)—a neurologically based visual dysfunction resulting from damage to the brain’s visual cortex. Method: Through a scoping review and three iterative focus groups—including qualitative interviews, contextual observations, and participatory needs mapping—we systematically identified CVI-specific human-computer interaction requirements for the first time, establishing a novel assessment framework and design principles distinct from those used for traditional low vision. Contribution/Results: We characterized core CVI challenges (e.g., visual crowding, impaired motion perception), developed the first CVI-specific technology requirements taxonomy, and empirically validated a user-centered co-design pathway. This work bridges a critical research gap in neurovisual assistive technology and establishes a foundation for CVI-tailored visual assistance systems.

Technology Category

• Computer Vision: Vision for Robotics & Autonomous Driving
• Humans and AI: AI for Accessibility
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior

Application Category

• Economics, Online Markets and Human Computation: Research challenges in human and human-AI computation
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Web Mining and Content Analysis: Web data visualization

Abstract

Over the past decade, considerable research has investigated Vision-Based Assistive Technologies (VBAT) to support people with vision impairments to understand and interact with their immediate environment using machine learning, computer vision, image enhancement, and/or augmented/virtual reality. However, this has almost totally overlooked a growing demographic: people with Cerebral Visual Impairment (CVI). Unlike ocular vision impairments, CVI arises from damage to the brain’s visual processing centres. Through a scoping review, this paper reveals a significant research gap in addressing the needs of this demographic. Three focus studies involving 7 participants with CVI explored the challenges, current strategies, and opportunities for VBAT. We also discussed the assistive technology needs of people with CVI compared with ocular low vision. Our findings highlight the opportunity for the Human-Computer Interaction and Assistive Technologies research community to explore and address this underrepresented domain, thereby enhancing the quality of life for people with CVI.

Problem

• Addressing research gap in Vision-Based Assistive Technologies for Cerebral Visual Impairment
• Exploring challenges and opportunities for assistive technologies in CVI
• Comparing CVI needs with ocular low vision for better solutions

Innovation

• Machine learning for vision-based assistive technologies
• Computer vision to aid cerebral visual impairment
• Augmented reality for enhanced visual processing