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

Bhanuka Gamage, Leona Holloway, Nicola McDowell, Thanh-Toan Do, Nicholas Price, Arthur Lowery, Kim Marriott

TL;DR

This paper investigates Vision-Based Assistive Technologies (VBAT) for Cerebral Visual Impairment (CVI), a brain-based visual dysfunction that remains underrepresented in VBAT research oriented toward ocular impairments. It combines a scoping review with three focus-group studies (seven CVI participants) to map current efforts, reveal seven major challenges, and contrast CVI needs with ocular low vision. The findings establish that CVI imposes high-level visual processing demands, often necessitating single-modality, low-load designs, and highlight a substantial gap in CVI-specific VBAT development and evaluation. The work calls for CVI-focused co-design and rigorous assessment of VBAT interventions, including AR/VR and image-enhancement strategies, while considering neuroplasticity effects and privacy, to meaningfully enhance independence and quality of life for people with CVI.

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.

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

TL;DR

This paper investigates Vision-Based Assistive Technologies (VBAT) for Cerebral Visual Impairment (CVI), a brain-based visual dysfunction that remains underrepresented in VBAT research oriented toward ocular impairments. It combines a scoping review with three focus-group studies (seven CVI participants) to map current efforts, reveal seven major challenges, and contrast CVI needs with ocular low vision. The findings establish that CVI imposes high-level visual processing demands, often necessitating single-modality, low-load designs, and highlight a substantial gap in CVI-specific VBAT development and evaluation. The work calls for CVI-focused co-design and rigorous assessment of VBAT interventions, including AR/VR and image-enhancement strategies, while considering neuroplasticity effects and privacy, to meaningfully enhance independence and quality of life for people with CVI.

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.

Paper Structure

This paper contains 22 sections, 4 figures, 4 tables.

Figures (4)

  • Figure 1: Overview of low-level and high-level visual difficulties for people with CVI. Note that this list is not exhaustive. Concepts depicted are extracted from lueck2015vision.
  • Figure 2: Summary of findings from the Scoping Review
  • Figure 3: Example enhancements demonstrated to the participants during the focus group discussion.
  • Figure 4: Image shared by P1 demonstrating the use of boundaries around objects to aid in subject and background separation.