News

Prof. Xiaoling HU’s research on “Sensorimotor Integration by Targeted Priming in Muscles with Electromyography-Driven Electro-vibro-feedback in Robot-Assisted Wrist/Hand Rehabilitation after Stroke” published in Cyborg and Bionic Systems

Research paper titled “Sensorimotor Integration by Targeted Priming in Muscles with Electromyography-Driven Electro-vibro-feedback in Robot-Assisted Wrist/Hand Rehabilitation after Stroke”, with Professor Xiaoling HU as the corresponding author, was recently published in Cyborg and Bionic Systems, an open access journal, published in association with BIT, that promotes the knowledge interchange and hybrid system codesign between living beings and robotic systems.   “Sensorimotor Integration by Targeted Priming in Muscles with Electromyography-Driven Electro-vibro-feedback in Robot-Assisted Wrist/Hand Rehabilitation after Stroke” Legeng Lin, Yanhuan Huang, Wanyi Qing, Man-Ting Kuet, Hengtian Zhao, Fuqiang Ye, Wei Rong, Waiming Li, and Xiaoling Hu* Cyborg and Bionic Systems. Vol 7. Article ID: 0507. doi: 10.34133/cbsystems.050   Abstract Restoring precise muscular control in the poststroke wrist/hand (W/H) demands sensorimotor integration to correct compensatory neuroplasticity. However, current rehabilitation robots inadequately modulate ascending somatosensory pathways from specific muscles. This study developed an electromyography (EMG)-driven soft robot with electro-vibro-feedback (EVF-robot) for targeted somatosensory priming in W/H muscles. This system integrates (a) focal vibratory stimulation and neuromuscular electrical stimulation for recruiting the somatosensory pathways of the targeted W/H flexors and extensors; (b) an EMG-driven control algorithm for strengthening the voluntary motor control of a driving muscle; and (c) robot assistance to achieve coordinated joint extension and flexion. In a single-arm trial with 20 sessions, 15 chronic stroke participants assisted by the system achieved significant improvements in voluntary W/H behavioral control, somatosensory feedback, and intermuscular coordination in the paretic upper limb (P

PolyU and The Beijing Institute of Technology sign MoU on 22 January 2026

The Hong Kong Polytechnic University (PolyU) and The Beijing Institute of Technology (BIT) signed a Memorandum of Understanding (MoU) to collaborate on propelling research and innovation in biomedical engineering, and nurturing talents in the field. Witnessed by Prof. Jin-Guang TENG, President of PolyU and Prof. Lan JIANG, President of BIT, the MoU was signed by Ir Prof. Ming ZHANG, Head of PolyU Department of Biomedical Engineering and Prof. Duanduan CHEN, Director of BIT Office of Hong Kong, Macao and Taiwan Affairs on 22 January 2026 at PolyU. This partnership is of great significance, as it not only catalyses the education and research development, but also fosters the international competitiveness by integrating the strengths of both parties to open a new chapter in biomedical engineering advancements.

The 6th Biomedical Engineering Innovation Competition "Love. Innovate for Happy Ageing" (第6屆生物醫學工程創意競賽之「愛．創耆樂」) Challenge Competition

Since 2019, the "Love. Innovate for Happy Ageing 愛．創耆樂" programme has upheld the mission of nurturing the younger generation to care for the elderly and to develop innovative solutions that improve the lives of seniors through technology. The programme consists of a Design Competition and a Challenge Competition, and has so far engaged around 2,000 young participants.   Following the inspiring Tencent STEAM for Good "Love. Innovate for Happy Ageing" Design Competition held last Saturday (10 January 2026), the 6th Biomedical Engineering Innovation Competition "Love. Innovate for Happy Ageing 愛．創耆樂" Challenge Competition, co-organised by the Hong Kong Sheng Kung Hui Welfare Council and the Department of Biomedical Engineering of PolyU, also reached a meaningful conclusion on 17 January 2026 at the PolyU campus.   Embodying the spirit of "Love. Innovate for Happy Ageing," the Challenge Competition is an intergenerational tournament featuring robotic cars in two categories: Micro:bit and LEGO. The competition includes team contests for Micro:bit and LEGO cars, as well as an individual contest for Micro:bit cars. In the team competitions, participants control their robotic cars to target the balloons attached to their opponents' cars while defending their own balloons. In the individual contest, participants use Micro:bit cars to navigate an obstacle course.   The event encouraged enthusiastic and inclusive participation from primary and secondary school students, as well as seniors over 60 years old. More than 70 teams took part in the Micro:bit and LEGO team competitions, while 77 individuals entered the Micro:bit individual competition, filling the day with laughter, excitement, and energy.   The programme continues to inspire young innovators to harness technology for the benefit of the ageing community, fostering a culture of care, creativity, and social responsibility - and bringing hope for a brighter, more inclusive future for all generations.   Visit Action Hub website for more details: https://actionhub.hk

The FattaLab® Fatty Liver Diagnostic Device, spearheaded by Ir Prof. Yongping ZHENG, has been awarded the 2026 Honoree in Digital Health at the Consumer Electronics Show (CES) Innovation Awards 2026

PolyU is committed to nurturing startups through its unique PolyVentures innovation ecosystem. These outstanding results are a testament to the University’s dedication to world-leading research and innovation, as well as its efforts to translate research excellence into impactful solutions that benefit society. Three innovations developed by PolyU and its startups have achieved remarkable success at the Consumer Electronics Show (CES) Innovation Awards 2026, garnering three prestigious Innovation Awards for their cutting-edge research and development achievements. One of the award-winning projects is the FattaLab® Fatty Liver Diagnostic Device, spearheaded by Ir Prof. Yongping ZHENG, Henry G. Leong Professor in Biomedical Engineering, Chair Professor of Biomedical Engineering, and Founder and Chief Scientist of Eieling Technology Limited. The FattaLab® Fatty Liver Diagnostic Device is the world’s first lightweight intelligent assessment system for fatty liver detection. Weighing only 120 grams, this palm-sized portable device, complemented by its mobile app, can complete fatty liver assessment within 30 seconds, achieving detection accuracy at medical-grade standards. This project has been awarded the 2026 Honoree in Digital Health of CES Innovative Awards 2026. Congratulations to Prof. ZHENG for this remarkable accomplishment! More: https://polyu.hk/kUgwO About CES Innovation Awards Organised annually by the Consumer Technology Association, CES is among the world’s most influential consumer electronics technology exhibitions, spotlighting cutting-edge electronic technology for modern living. The award-winning technologies alongside a diverse portfolio of forward-looking research innovations were showcased at CES 2026 held in Las Vegas on 6-9 January 2026.

Congratulations to Dr Man Ting AU on receiving the 2025 iCANX Young Scientists Award

Congratulations to Dr Man Ting AU on receiving the 2025 iCANX Young Scientists Award! Established in 2021, the iCANX Young Scientists Award is an annual award that recognises and honours leading young scientists worldwide for their exceptional achievements. As one of the three awardees from PolyU, Dr AU earned praise for her breakthrough research “Revitalising joints – In situ CAR Therapy for Osteoarthritis Treatment”. Her innovative Chimeric Antigen Receptor (CAR) therapy targets senescence in osteoarthritic joints, offering a long-lasting, disease-modifying treatment to halt progression and revitalise joint tissue without the need for surgery. Congratulations once again to Dr AU for her excellence in research innovation!

Tencent STEAM for Good "Love. Innovate for Happy Ageing" (騰訊小紅花科創家之「愛．創耆樂」) Design Competition

Since 2019, the "Love. Innovate for Happy Ageing 愛．創耆樂" programme has upheld the mission of nurturing the younger generation to care for the elderly and to develop innovative solutions that improve the lives of seniors through technology. The programme consists of a Design Competition and a Challenge Competition, and has so far engaged around 2,000 young participants.   This year, the Design Competition is co-organised by the Hong Kong Sheng Kung Hui Welfare Council, the Department of Biomedical Engineering of PolyU, Tencent Charity Foundation and Tencent WeTech Academy.   Themed "Tech for Good", the Tencent STEAM for Good "Love. Innovate for Happy Ageing" Design Competition and Award Ceremony was held on 10 January 2026 at The Hong Kong Polytechnic University.   In addition to interactive sessions with seniors to understand their real needs and workshops on gerontechnology product design, the programme introduced new elements including STEAM workshops on Sports Science & Biomedical Engineering / Artificial Intelligence & Smart Home / Sensors & Robots, as well as in-school support sessions. Over 200 elderly participants were recruited and 15 “Gerontechnology Supervisors” were trained to offer guidance to the participating teams on how to better address the needs of seniors.   Over 100 teams across primary, secondary, and open categories submitted their proposals. After a preliminary selection, 60 teams with more than 260 participants competed on 10 January for 16 awards and 3 “Love. Innovate for Happy Ageing” scholarships.   The winning teams were selected based on their products' STEAM application, usability, and potential for improvement by a judging panel comprising over 250 biomedical engineers, representatives from technology companies, and elderly end-users. Scholarships were also awarded to teams whose products were considered suitable for further research and development, encouraging them to continue designing products using STEAM knowledge and skills.   The programme continues to inspire young innovators to harness technology for the benefit of the ageing community, fostering a culture of care, creativity, and social responsibility - and bringing hope for a brighter, more inclusive future for all generations.   Visit Action Hub website for more details: https://actionhub.hk

Prof. Emma Shujun WANG’s research on “Knowledge-guided adaptation of pathology foundation models effectively improves cross-domain generalization and demographic fairness” published in Nature Communications

Research paper titled “Knowledge-guided adaptation of pathology foundation models effectively improves cross-domain generalization and demographic fairness”, with Professor Emma Shujun WANG as one of the corresponding authors, was recently published in Nature Communications, an open access, multidisciplinary journal dedicated to publishing high-quality research in all areas of the biological, health, physical, chemical, Earth, social, mathematical, applied, and engineering sciences.   “Knowledge-guided adaptation of pathology foundation models effectively improves cross-domain generalization and demographic fairness” Yanyan Huang, Weiqin Zhao, Zhengyu Zhang, Yihang Chen, Yu Fu, Feng Wu, Yuming Jiang, Li Liang*, Shujun Wang* and Lequan Yu* Nature Communications 16: 11485 (2025). doi: 10.1038/s41467-025-66300-y   Abstract Foundation models in computational pathology suffer from site-specific and demographic biases, which compromise their generalizability and fairness. We introduce FLEX, a framework that employs a task-specific information bottleneck, guided by visual and textual domain knowledge, to disentangle robust pathological features from these artifacts. Using three large cohorts (The Cancer Genome Atlas, Clinical Proteomic Tumor Analysis Consortium, and an in-house dataset) across 16 clinical tasks, totaling over 9,900 slides, we demonstrate that FLEX achieves superior zero-shot generalization to unseen external cohorts, significantly outperforming baselines and narrowing the performance gap between seen and unseen domains. A comprehensive fairness analysis confirms that FLEX also effectively mitigates disparities across demographic groups. Furthermore, its versatility and scalability are proven through compatibility with various foundation models and multiple-instance learning architectures. Our work establishes FLEX as a promising solution for developing more generalizable and equitable pathology AI for diverse clinical settings.

PolyU BME welcomes new Professor Prof. Nanguang CHEN

PolyU BME warmly welcomes Prof. Nanguang CHEN, our new Professor! Prof. CHEN received his BEng in Electromagnetic Measurement and Instrumentation from Hunan University in 1988, MSc in Theoretical Physic from Peking University in 1994, and PhD in Biomedical Engineering from Tsinghua University in 2000. His research interests include deep learning approach to inverse problem/image reconstruction, AI assisted resolution enhancement, laser speckling imaging, lightsheet microscopy, optical coherence tomography, microcirculation imaging, diffuse optical tomography for breast cancer detection, functional near-infrared spectroscopy for mental disease diagnosis/staging, and portable/wearable optical spectroscopy/imaging devices.

Congratulations to Prof. Chunyi WEN for receiving the 2025/26 RGC Collaborative Research Project Grant (CRPG)

Hong Kong's Research Grants Council (RGC) has recently announced the funding results of the Collaborative Research Fund (CRF) in 2025/26, including Collaborative Research Project Grant (CRPG), Collaborative Research Equipment Grant (CREG) and Young Collaborative Research Grant (YCRG). PolyU has three CRPG, two CREG and two YCRG full proposals supported by RGC in the 2025/26 exercise. One of the three CRPG funded projects is Prof. Chunyi WEN's proposal titled "Endothelins in mechanoaging and osteoarthritis: biomarker discovery and drug development",  Congratulations to Prof. WEN!   About the Collaborative Research Fund (CRF) The Collaborative Research Fund (CRF) aims to encourage research groups in UGC-funded universities to engage in collaborative research across disciplines and across universities, with a view to enhancing the research output of universities in terms of the level of attainment, quantity, dimensions and/or impact. In assessing proposals, the Research Grants Council (RGC) puts emphasis on capacity building and the potential of a proposal to develop into an area of research strength.

Prof. Puxiang LAI’s research on “Speckle-Driven Single-Shot Orbital Angular Momentum Recognition with Ultra-Low Sampling Density” published in Nature Communications

Research paper titled “Speckle-driven single-shot orbital angular momentum recognition with ultra-low sampling density”, with Professor Puxiang LAI as one of the corresponding authors, was recently published in Nature Communications, an open access, multidisciplinary journal dedicated to publishing high-quality research in all areas of the biological, health, physical, chemical, Earth, social, mathematical, applied, and engineering sciences.   “Speckle-driven single-shot orbital angular momentum recognition with ultra-low sampling density” [Zhiyuan Wang, Haoran Li], Tianting Zhong, Qi Zhao, Vinu R. V, Huanhao Li, Zhipeng Yu, Jixiong Pu, Ziyang Chen*, Xiaocong Yuan*, and Puxiang Lai* Nature Communications 16(1): 11097 (2025). doi: 10.1038/s41467-025-66074-3   Abstract Recognizing orbital angular momentum (OAM) of vortex beams is essential for optical communications and quantum technologies, yet conventional methods struggle in scattering environments and rely on high-resolution sensors. In this study, we propose a speckle-driven OAM recognition technique termed spatially multiplexed points detection (SMPD), which transforms scattering media into efficient encoders. By extracting intensity information from only 16 spatially distributed points in a speckle plane, SMPD achieves over 99% recognition accuracy with a sampling density as low as 0.024%—4096 times lower than conventional imaging-based approaches. The method also demonstrates versatility in spatiotemporally interleaved vortex beam decoding, high-capacity OAM-multiplexed communication, and classification tasks such as MNIST and Fashion-MNIST. This work establishes a scalable, resource-efficient strategy for optical information processing and sensing in complex scattering environments. Eight vortex beams with different OAM values are sequentially transmitted through the MMF. The charge couple device (CCD) camera records a single speckle image, representing the spatiotemporally superposition of all beams, synchronized with a digital mirror device (DMD) and a function generator. A sampling mask is applied, and the sampled intensities are input into a modified ROAM-ANN to decode the OAM information.