Institution profile

Monash University

Academic institutionaustralasia · au
Official website
Research library1,269linked papers
Opportunities0open roles
Selected work

Representative Papers

3D Printed Maps and Icons for Inclusion: Testing in the Wild by People who are Blind or have Low Vision

Oct 24, 2019International ACM SIGACCESS Conference on Computers and Accessibility

People with visual impairments face significant challenges in independent navigation and spatial orientation. Method: This study introduces a 3D-printed tactile map and icon system designed to support on-site orientation and mobility training. Employing an iterative, user-centered design process, we conducted field-based human factors evaluations—including tactile recognition and spatial cognition assessments—in real-world public environments, marking the first empirical validation of 3D tactile maps in authentic settings. Contribution/Results: Results demonstrate that realistic 3D icons are accurately recognized without legends, significantly enhancing mental map construction. The system improves users’ spatial cognition, autonomous navigation proficiency, and sense of belonging. Furthermore, the study distills empirically grounded guidelines and reusable design principles for inclusive design, offering both methodological frameworks and technical pathways to advance accessible built environments.

32 citations3 influentialRead paper

What do Blind and Low-Vision People Really Want from Assistive Smart Devices? Comparison of the Literature with a Focus Study

Oct 22, 2023International ACM SIGACCESS Conference on Computers and Accessibility

This study investigates whether current AI-based assistive device research aligns with the authentic needs of blind and low-vision (BLV) individuals. Method: We conducted a systematic literature review of 646 papers and in-depth interviews with 24 BLV users, integrating bibliometric analysis, user-need prioritization, and Spearman rank correlation testing. Contribution/Results: Our analysis reveals, for the first time, only a weak correlation between prevalent academic task formulations—such as object detection and image captioning—and actual user preferences. Instead, the top five most frequently cited needs center on real-time scene understanding and natural language–based conversational interaction. Users strongly prefer head-mounted, lightweight, and minimally intrusive devices. These findings challenge the dominant vision-centric paradigm in assistive AI research and provide empirical grounding for a user-centered design shift—emphasizing contextual awareness, multimodal interaction, and ergonomic form factors—thereby informing more effective, human-centered assistive technology development.

23 citations1 influentialRead paper

人工智能醫學應用的前景與風險

Jan 01, 2019International Journal of Chinese & Comparative Philosophy of Medicine

This study critically examines AI’s dual impact in healthcare: its transformative potential in genomics and public health, alongside profound ethical and institutional risks—including privacy breaches, algorithmic bias, physician deskilling, and imbalanced human–machine decision authority. Moving beyond technocentric paradigms, it introduces two foundational conceptual contributions: the reconfiguration of care as “datafied caregiving” and the normative calibration of “machine recommendation weight,” both grounded in philosophy of technology and bioethics. Employing an interdisciplinary analytical framework integrating medical ethics, philosophy of science, health policy, and big-data governance, the study uncovers structurally embedded risks overlooked in prevailing discourse. Its key contribution lies in reframing global regulatory and ethics review frameworks to center transparency, redistribution of epistemic and decisional authority, and preservation of clinical agency as core evaluative criteria.

18 citations2 influentialRead paper

Sports-QA: A Large-Scale Video Question Answering Benchmark for Complex and Professional Sports

Jan 03, 2024arXiv.org

Existing VideoQA datasets lack fine-grained modeling of professional sports actions, hindering effective reasoning for descriptive, temporal, causal, and counterfactual questions. To address this, we introduce Sports-QA—the first video question answering benchmark tailored to professional sports scenarios—covering multiple sports disciplines and four categories of complex reasoning tasks. Methodologically, we propose the Auto-Focus Transformer (AFT), which employs an attention-driven dynamic focusing mechanism to adaptively model multi-scale temporal information and integrates joint video–language representation learning. Extensive experiments demonstrate that AFT achieves state-of-the-art performance on Sports-QA, substantially outperforming general-purpose VideoQA models. This work constitutes the first systematic validation of an architecture explicitly designed for fine-grained sports action understanding and dynamic logical reasoning, establishing a new foundation for domain-specific VideoQA research.

10 citations2 influentialRead paper
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