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    Vision-Based Assistive Technologies for People with Cerebral Visual Impairment: A Review and Focus Study
    (Association for Computing Machinery, 2024-10-27) Gamage B; Holloway L; McDowell N; Do T-T; Price N; Lowery A; Marriott K
    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 signifcant 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 fndings 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.
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    Identifying potential for decision support tools through farm systems typology analysis coupled with participatory research: A case for smallholder farmers in Myanmar
    (MDPI (Basel, Switzerland), 2021-06-02) Thar SP; Ramilan T; Farquharson RJ; Chen D; Gröngröft A
    Decision Support Tools (DSTs) in agriculture have been widely developed but have not been well accepted by smallholder farmers. One reason for the limited use is that the tools do not account for the complexity of heterogeneous smallholder farming systems. Identifying farm typologies has facilitated technology transfer to target groups of farmers. Accounting for heterogeneity in farm systems can help in designing and deploying DSTs to address farmer needs. Typology analysis was applied to a 600-household survey dataset to identify different farm system types. Qualitative participatory research was used to assess the potential deployment of DSTs for fertilizer management. Six types of farm systems were identified with distinct characteristics in the study area of central Myanmar. Participatory research through focus group discussions with 34 participants from the six different farm types validated the farm typologies and found that farmers from one type considered that DSTs could be useful in gaining more information and knowledge. An important finding was that DSTs providing prescriptive advice were inconsistent with what many farmers want. Farmers indicated that discussion groups are a preferred learning-based approach rather than a prescriptive tool. Farmers preferred video clips and infographics integrated into existing familiar digital platforms. This study identifies heterogeneity within a large farm sample and develops a deeper understanding of fertilizer decisions as well as knowledge and intentions related to the use of DSTs or apps via follow-up focus group discussions. Incorporating a participatory research framework with typology identification can have a beneficial role in direct interactions with smallholders that may increase their acceptability of DSTs. This study has generated valuable information about farmer types and serves as a starting point for developing a framework for discussion support systems that may better relate to the needs of farmers.