MAE research teams recently received 3 awards at the 5th Asia Exhibition of Innovations and Inventions Hong Kong (AEII).

The AEII is a collaboration between Palexpo, the organiser of the International Exhibition of Inventions Geneva, and the Hong Kong Exporters’ Association. The event aims to promote Hong Kong as a knowledge-based economy by leveraging its strength as a hub for innovation and technology. The fifth edition took place from 4 to 5 December 2025 at the Hong Kong Convention and Exhibition Centre and featured more than 135 inventions and product innovations.

Gold Medal: Lightweight low-cost cross-joint assistive exoskeleton

Principal investigator and team members:
Professor Liao Wei-hsin, Dr. Liao Hongpeng, Dr. Hugo Chan Hung-tin, Mr. Muhammad Rumi Wasir, Mr. Lee Pak-ho, Mr. Onuoha Chika

Project description:
This next-generation wearable exoskeleton directly addresses urgent market needs: labour shortages from an ageing workforce, widespread musculoskeletal injuries, and the lack of accessible daily-assistive devices. Unlike traditional exoskeletons that are heavy, expensive, and complex, it features lightweight, affordable, modular multi-joint assistance, enabling broad adoption in both industrial environments and daily life. The cross-joint exoskeleton delivers coordinated assistance to the arms, back, and legs, providing effective support for construction, logistics, caregiving, and mobility, and significantly reducing injury risks and physical fatigue. Its unique hybrid drive, modular architecture, and AI-powered adaptive control ensure comfort, intuitive use, and personalised support. This innovation transforms exoskeletons from niche, professional equipment into practical everyday tools, empowering users to work and live more healthily, safely, and efficiently.

Dr. Liao Hongpeng (right) received a Gold Medal on behalf of the team.

Gold Medal: Mass customisation of breathable lightweight hip protectors for older people via automatic inverse design and 3D printing

Principal investigator and team members:
Professor Song Xu, Dr. Ding Junhao, Dr. Winston Ma Waishing, Dr. Qu Shuo, Dr. Ye Haitao, Mr. Mo Haoming, Mr. Hu Zongxin, Mr. Niu Tianxiao

Professor Yang Yi-Jian, Dr. Zheng Xiaoping (Department of Sports Science and Physical Education, CUHK)

Project description:
Hip fractures represent the most serious fall-related injury risk for older people. Consistent use of hip protectors is key to reducing this risk, but traditional designs are often bulky, thick, non-breathable, expensive, and limited in size, frequently deterring older adults from wearing them due to discomfort and inconvenience.

Addressing these issues, the team has developed a new generation of breathable, lightweight protectors specifically for seniors. Using 3D-scan-based inverse design and AI-automated lattice generation, this approach enables 3D printing of protectors with an optimised, lightweight structure. This minimises weight and cost while maximising comfort. Easily attachable to trousers, these protectors effectively reduce the risk of hip fractures from falls. Their breathable, lightweight design is particularly well-suited to elderly individuals living in hot and humid climates.

Mr Mo Haoming (right) received a Gold Medal on behalf of the team.

Silver Medal: Embodied AI-driven cooperative multi-UAV search and rescue

Principal investigator and team members:
Professor Chen Benmei, Professor Chen Xi, Dr. Cao Haosen, Mr. Shao Jingheng, Mr. Wang Pei, Mr. Huang Yijun

Project description:
In Hong Kong, numerous hiking emergencies occur in environments where locating victims visually is challenging, creating serious risks to human life and placing heavy demands on rescue resources. To address this challenge, the team proposed a novel multimodal search and rescue system that leverages the coordination of multiple UAVs to autonomously locate missing people in complex terrain. Each UAV carries an on-board base station capable of locating individuals through IMSI-based methods or by deriving their likely position from uplink radio signals. These radio-derived estimates establish an initial search range to guide cooperative UAV deployment. Complementing this capability, airborne optical sectioning penetrates canopy cover to reveal hidden ground features, with vision models aiding in potential victim detection. Use of a large language model (LLM) further enhances adaptive path planning by semantically integrating multimodal inputs. By combining radio localisation, optical sensing, and LLM-driven adaptation, the system substantially improves rescue efficiency and reliability in inaccessible environments.

CUHK Press Release: Click here!