Prof. Qian ZHANG

Award Citation

Hong Kong is a super-aging society, resulting in an undersupply of elderly care services. Hong Kong government has launched an elderly care policy that emphasizes “home care as the basis”. Solutions integrating AI and IoT technologies have become essential for monitoring the health of the elderly in homes and communities. These solutions adopt two primary approaches. The first approach involves wearable devices, but it has limitations as elderly may not wear them constantly. The second approach uses surveillance cameras, with the shortcomings such as privacy violations and inadequate monitoring of high-risk areas such as bathrooms.

Millimeter wave (mmWave) radar technology is capable of measuring target distance using electromagnetic waves. It offers non-contact monitoring and privacy protection, and can capture movement, body posture, and more. This technology has the potential to continuously monitor and assess the vital signs of the elderly, detect abnormal behavior, and provide personalized healthcare.

This project is to develop a framework for human-centric contactless sensing using mmWave signals. We aim to address the limitations of current mmWave radar systems, such as limited sensing capabilities, vulnerable signal quality, insufficient information from a single radar, and the lack of robust and efficient AI algorithms. Our approaches are as follows: (1) Developing advanced radar systems with a cascade design; (2) improving signal quality with reconfigurable intelligent surface and virtual antenna array techniques; (3) utilizing networked mmWave sensors for broader coverage; and (4) designing AI algorithms tailored for mmWave sensing that consider the physical characteristics of mmWave signals.

Through contactless sensing, this project will provide solid support for long-term care services for the elderly. We will showcase the superiority of our proposal by demonstrating its effectiveness in two typical tasks: vital sign monitoring and abnormality detection.