Best URIS Research Project Award 2024

The Best URIS Research Project Award aims to recognise and reward outstanding URIS research projects that have demonstrated remarkable research achievements. Each year, one Grand Award and two Merit Award will be presented. Nominations for the Award are invited in October every year.

Nomination Eligibility

URIS research projects completed between 1 September 2023 and 31 August 2024 with the highest rating in the completion report are eligible for nomination.

Selection Criteria

A selection panel has thoroughly assessed and evaluated the nominations for the Award. The selection criteria are as follows:

Achievements and impact of the URIS research project; and
Research outputs and prestigious awards resulting from the project.

Grand Award

Project Title: Effectiveness of a telecare-based intervention in supporting the informal caregiver of community-dwelling older adults: a pilot study

Chief Supervisor: Dr WONG Kwan Ching Arkers | School of Nursing

The project emerged from a heartfelt concern for primary caregivers who often go unrecognized in our communities. While much attention is given to the well-being of older adults, the critical needs of those providing care are frequently overlooked. This research highlights the indispensable role of informal caregivers and emphasizes the necessity of supporting them to prevent caregiving stress and burnout. Through the study, caregivers received personalized nurse case management, Zoom meetings, an informative website, and a collaborative discussion platform. As a result, they experienced significant boosts in confidence and reductions in feelings of despair. By demonstrating remarkable improvements in both the physical and emotional well-being of caregivers, this study advocates for the implementation of tailored telecare interventions. These enhanced support mechanisms not only ensure the well-being of caregivers and the older adults they care for, but also represent a shift towards a more holistic and sustainable healthcare ecosystem.

This research highlights the transformative capacity of telecare interventions tailored for informal caregivers, empowering them with accessible and timely support to enhance their overall physical and psychological health.

School of Nursing

NG Nga Ping

Merit Award

Project Title: Older adults’ perceptions and acceptability toward the use of artificial intelligence (AI)-based health technologies: a qualitative study

Chief Supervisor: Dr WONG Kwan Ching Arkers | School of Nursing

Artificial Intelligence (AI) is revolutionizing healthcare by enhancing efficiency and accuracy. As our population ages, it’s essential to understand how older adults perceive AI-based health technologies for self-monitoring and maintenance. My URIS project tackled this by gathering crucial insights from older adults, revealing their views on AI health tech and identifying what helps or hinders their use of these tools. Using well-established behavior change models, we developed recommendations for creating user-friendly and secure AI health technologies that could support human care. I'm dedicated to continuing this vital work to improve the integration of AI in elder care.

Understanding older adults' perspectives on AI-based health technologies, paving the way for future tailored solutions and interdisciplinary collaboration.

School of Nursing

LEE Hiu Toon Jessica

Merit Award

Project Title: A high-definition map generation with traffic signs based on LiDAR-Visual-IMU fusion SLAM method

Chief Supervisor: Dr HSU Li-ta | Department of Aeronautical and Aviation Engineering

A high-definition map (HD map) is a crucial component of autonomous driving and intelligent transportation systems. However, generating HD maps for large-scale urban environments involves enormous data volumes—reaching hundreds of terabytes—leading to significant manual costs for data extraction and labelling. This research introduces a novel method for HD map generation by integrating semantic segmentation with SLAM algorithms. The system combines data from LiDAR, cameras, and IMU sensors to construct 3D colour scenes, while the images features are extracted and labelled such as lanes, traffic lights, and road signs through semantic segmentation and then fused into the 3D scene, producing an HD map embedded with labelling information. This approach provides a scalable solution for acquisition and generating segmented and labelled training data, essential for autonomous driving development. In the future, advanced multi-modal AI technologies will be integrated to fuse higher-dimensional data, and the system has the potential to be deployed on drones, enabling more flexible and efficient mapping.

A High-definition Map generation method based on the integration of semantic segmentation and SLAM algorithms was developed, providing new possibilities for large-scale data resources in intelligent transportation and autonomous driving.