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AIoT-powered fire safety: Transforming smart buildings with real-time digital twins

Fire, especially in skyscrapers, highlights the urgent need for smarter, faster and more reliable firefighting solutions tailored to these high-tech spaces. In smart buildings, where every second counts and the stakes are higher, conventional fire detection and response methods are no longer adequate.  Prof. Asif Sohail USMANI, Chair Professor in Building Sciences and Fire Safety Engineering of the Department of Building Environment and Energy Engineering at The Hong Kong Polytechnic University, and his research team address these challenges by introducing a novel approach that leverages Artificial Intelligence of Things (AIoT) and Digital Twin technologies for super real-time fire forecasting and management.  AIoT is the integration of AI and the Internet of Things (IoT). IoT is a network collecting data from sensors and transmitting the data through the internet. It is widely used in fire detection systems to collect real-time data such as smoke, heat and gas, and to optimise emergency response. With AI embedded into IoT, AIoT can analyse and learn the data for more accurate forecasting. Digital Twin is a virtual representation (a virtual twin) of physical objects or systems. It uses real-time data to accurately reflect the real-world situation. The AIoT-integrated Digital Twin system is designed to bridge the gap between fragmented sensor data and comprehensive situational awareness, enabling decision-makers to anticipate fire dynamics and coordinate effective responses in complex building environments. The research titled, “AIoT-powered building digital twin for smart firefighting and super real-time fire forecast,” was published in Advanced Engineering Informatics. This integration of IoT hardware and AI enables rapid detection of fire hazards, supports automated safety responses and provides the foundation for super real-time forecasting of impending critical events and decision-making within the Digital Twin environment. The AutoDecoder Long Short-term Memory Neural Network (ADLSTM-Fire), a hybrid deep learning model the team developed, processes these sensor data to reconstruct high-dimensional temperature fields and forecast future developments up to 60 seconds in advance. By combining AutoDecoder and Long Short-Term Memory (LSTM) neural networks, the model transforms sparse sensor inputs into detailed spatiotemporal maps of fire progression. This predictive capability is essential for smart buildings, where early warnings and dynamic risk assessment can prevent escalation and guide evacuation strategies. Information interaction within the system is managed through a multi-layered architecture, encompassing physical sensing, virtual data processing and user application interfaces. Sensor data are transmitted to a local router and uploaded to a cloud server, where the ADLSTM-Fire model operates within a Digital Twin platform. This platform, integrated with Building Information Modelling, offers a user-friendly interface for visualising temperature distributions, identifying hazardous regions and issuing commands for physical interventions. The modular design ensures seamless communication between hardware, software and users, supporting both real-time monitoring and strategic decision-making. Test results from numerical simulations and real-world experiments demonstrate the system's accuracy and robustness. The real-time reconstruction model achieved an accuracy of 93%, while the ADLSTM-Fire advance forecast model reached 92%. Both models predicted the spatial and temporal evolution of temperature fields with inference times under 0.5 seconds, delivering super real-time insights into fire dynamics.  The integration of AIoT and Digital Twin technology marks a significant advancement in fire safety for smart buildings. The demonstrated accuracy, speed and adaptability of the ADLSTM-Fire model highlight its potential to enhance urban resilience, reduce fire casualties and support the development of safer, smarter cities. As research continues to refine these models and expand their applicability, AIoT-driven fire safety systems are poised to become an essential component of future urban infrastructure. Prof. Usmani has, for 30 years, primarily worked in the field of fire safety engineering and structural fire engineering. In 2020, his proposed project "SureFire: Smart Urban Resilience and Firefighting" was awarded HK$ 33.33 million from the Hong Kong Research Grants Council Theme-based Research Scheme. As an extension of FireGrid, SureFire is developed typically for large building compartments like the skyscrapers commonly seen in Hong Kong.  The AIoT-integrated Digital Twin system in this study is part of the SureFire system. The team's work was awarded the 2026 Philip Thomas Medal of Excellence for the best paper presented at IAFSS 2023, which was titled "Introducing an active opening strategy to mitigate large open-plan compartment fire development."  Source: Innovation Digest 7

PolyU computer-vision scientist honoured with 2026 Frontiers of Science Award by International Congress of Basic Science

Prof. ZHANG Lei John, Chair Professor of Computer Vision and Image Analysis of the Department of Computing at The Hong Kong Polytechnic University (PolyU), has been honoured with the prestigious 2026 Frontiers of Science Award presented by the International Congress of Basic Science (ICBS). This accolade recognises his outstanding research and major contributions to the field of image processing and computer vision. Prof. Zhang was recognised in the “Information Sciences and Engineering” category for his paper “Beyond a Gaussian denoiser: residual learning of deep CNN for image denoising” published in IEEE Transactions on Image Processing (2017). He shares this distinction with co-authors from Harbin Institute of Technology, ULSee Inc., and Xi’an Jiaotong University. Prof. Zhang’s research interests focus on computer vision, image and video analysis, deep learning, etc. As of 2026, his publications have attracted more than 130,000 citations. He was named a “Clarivate Analytics Highly Cited Researcher” consecutively from 2015 to 2025. He has also served as a (Senior) Associate Editor and (Senior) Area Chair for several top-tier international journals and conferences. Beyond academia, Prof. Zhang’s research has been successfully translated into commercial products such as OPPO’s flagship smartphone series Find X7, X8 and X9. The Frontiers of Science Award recognises exceptional, original research published over the past decade across Mathematics, Physics, and Information Science & Engineering. To receive this honour, a scientific achievement must be of the highest scholarly value and have made a major global impact in its field. Learn more about Prof. Zhang’s research: A vision to enhance image quality and analysis

PolyU develops Hong Kong’s first screening tool to detect social frailty in seniors

Loneliness and social isolation are widely recognised as harmful to elders’ health, yet frontline care workers have long lacked a simple, objective way to identify those most at risk. Researchers at The Hong Kong Polytechnic University (PolyU) have developed a set of community-based, preventive approaches to addressing social frailty among seniors, including a new screening tool and nature-based interventions in care facilities. This work responds directly to the Government’s policy shift from treatment towards prevention and community-based care. Social frailty refers to a state of vulnerability arising from a lack of social support, resources, connectedness and the fulfilment of basic needs. It is linked to poorer health and may increase the risk of physical frailty. Until now, the absence of standardised terminology and objective measurement tools has made these subjective experiences difficult to consistently assess. To close this gap, the team led by Prof. Jed MONTAYRE, Associate Head (Strategy) and Associate Professor of the School of Nursing, developed the Social Frailty 10-Item screening tool (SF-10). Brief in design for busy clinical settings, it helps enable frontline workers to identify at-risk older adults early and direct them to appropriate help. Supported by the General Research Fund of the Research Grants Council, the study was published in Contemporary Nurse, and social workers in non-governmental organisations and community clinics have already adopted the tool. Prof. Montayre said, “Recognising social frailty as a risk factor opens up a more holistic understanding of ageing. It positions social well-being on par with physical health, acknowledging that the two are inextricably linked and that addressing social risks can profoundly improve overall health outcomes.” In developing SF-10, the team used a co-design approach to make it both rigorous and non-stigmatising, conducting two rounds of workshops with 40 diverse stakeholders, including older adults, family caregivers, social workers, occupational therapists, nurses and doctors. 234 community-dwelling seniors were evaluated with the resulting tool, which demonstrated promising reliability and validity, with further validation currently under way. SF-10 assesses five domains: general resources, social participation, social connections, interpersonal relationships and self-management. Each is rated on a five-point scale, with higher scores signalling greater risk. The results guide practical next steps: low scores for general resources can trigger referral to financial or healthcare support, while low social participation scores can point individuals towards community programmes or social prescribing  — a holistic approach promoted by the World Health Organisation of referring people to non-medical community activities to improve their well-being. “SF-10 serves as a practical instrument for longitudinal care tracking in fast-paced environments like primary care, community services and home-based care, providing a solid foundation for social prescribing,” said Prof. Montayre. “By administering periodic screening, care networks can track how an individual’s social risk profile changes over time. We hope it will eventually become a standardised assessment across Hong Kong.” In related work that reinforces the importance of social connectedness, Prof. Montayre’s team also conducted a comprehensive systematic review and meta-analysis of nature-based interventions for reducing agitation in older people with dementia in care settings. Drawing on global evidence from both direct nature experiences, such as garden visits, and indirect exposures, such as circadian lighting, natural sounds or scents, it identifies physical settings and social interaction as the two critical drivers of clinical effectiveness. The relevant work is published in Healthcare.    Indoor interventions proved more effective than outdoor ones, while interventions involving social interaction outperformed those without. Notably, unregulated outdoor stimulation sometimes had negative effects, demonstrating that environmental designs must keep residents of care facilities engaged but not overwhelmed. “Non-pharmacological measures such as nature-based interventions can enhance social well-being and help prevent social frailty, particularly among those experiencing cognitive decline,” Prof. Montayre noted. “Having the space to socialise through such programmes significantly enhances social connectedness among older adults.” The findings offer practical solutions for high-density cities like Hong Kong, laying the groundwork for future dementia-friendly design guidelines. Because individuals with dementia respond differently depending on their tolerance for stimulation, Prof. Montayre stressed that environmental support must be tailored to the person. The research team is continuing to expand validation of SF-10 and to work with community partners to translate these findings into routine practice, contributing to a more preventive, sustainable and person-oriented model of care for Hong Kong’s ageing population.

Chongqing University Renji Hospital delegation visited PolyU to explore collaboration on bio-welding surgical robots

A delegation led by Dr Sun Zhongyi, Director of the Urogenital Medical Center at Chongqing University Renji Hospital (The Fifth Peoples Hospital of ChongQing), visited The Hong Kong Polytechnic University (PolyU) on 17 August for in-depth exchanges on cutting-edge bio-welding surgical systems and robotics technology. The visit explored new opportunities for medical-engineering integration and industry-academia-research collaboration. During the exchange meeting, both parties introduced their team members and latest developments. Participants engaged in fruitful discussions on the translation and future development of core bio-welding technologies. PolyU representatives presented the University’s research strengths in intelligent robotics, artificial intelligence (AI), and interdisciplinary research at the interface of medicine and engineering. The Renji Hospital delegation shared progress in the development and clinical applications of bio-welding technology. The delegation also visited PolyU Artificial Intelligence and Robotics Laboratory, gaining first-hand insights into the University's research capabilities and talent development in AI and robotics, while exploring the application potential of cutting-edge robotic technologies in healthcare. Looking ahead, PolyU will continue to strengthen ties with Chongqing University Renji Hospital, leveraging interdisciplinary strengths in medicine, precision engineering and AI to advance collaborative R&D and accelerate innovation in bio-welding surgical robots, contributing to the future of medical technology.  

PolyU young mathematics scholar honoured with the 2026 Frontiers of Science Award by the International Congress of Basic Science

Prof. Zhang Shijun, Assistant Professor of the Department of Applied Mathematics at The Hong Kong Polytechnic University (PolyU), has been honoured with the prestigious 2026 Frontiers of Science Award presented by the International Congress of Basic Science (ICBS) in recognition of his outstanding research, which demonstrates profound impact and forward-looking vision. Prof. Zhang was recognised in the mathematics (numerical analysis) category for his paper, "Deep network approximation for smooth functions," published in the SIAM Journal on Mathematical Analysis (2021). He shares this distinction with co-authors from Duke University, the National University of Singapore, and the University of Maryland. Prof. Zhang’s research focuses on advancing the theoretical foundations and practical capabilities of deep neural networks. His work examines how neural networks approximate complex functions, aiming to minimise critical approximation, generalisation, and optimisation errors, thereby bridging the gap between theory and application. Beyond these core principles, his research spans neural network optimisation, activation function properties, and the expressive power of modern architectures such as Transformers and Graph Neural Networks. He also explores cutting-edge techniques, including transfer learning, neural architecture search, and model compression, to drive next-generation innovations in artificial intelligence. The Frontiers of Science Award recognises exceptional, original research published over the past decade across Mathematics, Physics, and Information Science & Engineering. To receive this honour, a scientific achievement must be of the highest scholarly value and have made a major global impact in its field.  

PolyU research team develops trustworthy AI framework TRUECAM to enhance reliability of pathology AI in cancer diagnosis

Cancer diagnoses directly affect treatment decisions and prognosis and can even have life-altering consequences. Traditional pathological diagnosis primarily relies on pathologists examining and analysing tissue slides under a microscope and making judgements based on their professional experience. In recent years, the application of artificial intelligence (AI) in pathology diagnosis (pathology AI) has advanced rapidly, helping to improve diagnostic efficiency and support clinical decision-making. However, many existing AI models still lack a complete and verifiable mechanism to ensure the reliability of diagnostic outcomes, limiting the application of such technology in high-stakes clinical settings. To tackle this challenge, Prof. ZHANG Xiaoge, Assistant Professor of the Department of Industrial and Systems Engineering at The Hong Kong Polytechnic University (PolyU), and his research team have developed an integrated AI framework named TRUECAM (TRustworthiness-focused, Uncertainty-aware, End-to-end CAncer diagnosis with Model-agnostic capabilities). Applied to whole-slide image analysis for non-small cell lung cancer subtyping, the framework ensures both data and model trustworthiness, laying an important foundation for the safe application of pathology AI in cancer diagnosis. TRUECAM is designed to enhance the reliability of AI-assisted cancer diagnosis. It can assess the level of the AI’s confidence in its diagnostic outputs and proactively prompt pathologists to review cases when uncertainty is high or when input data fall outside the model’s scope. At the same time, the framework is model-agnostic, supporting the complete analysis pipeline from pathology images to diagnostic outcomes and helping healthcare professionals apply AI-generated diagnoses more reliably. The framework is applied to whole-slide imaging, a process that digitally scans glass tissue slides into high-resolution digital images and provides “virtual microscopy”, allowing pathologists to review pathology samples on a computer. The findings show that TRUECAM is applicable not only to non-small cell lung cancer subtyping, but can also be extended to breast, brain, and kidney cancer subtyping tasks, as well as a 46-class pan-cancer slide-level classification setting, demonstrating its broad application potential and clinical translational value. As a general framework, TRUECAM can be integrated into pathology AI models of various sizes, architectures, purposes and complexities to support responsible clinical applications. The framework has three core functions: detecting out-of-scope inputs, automatically eliminating highly ambiguous and difficult-to-judge image regions, and applying conformal prediction to keep diagnostic error rates within an acceptable range. The research team conducted a systematic evaluation of the framework across multiple cancer datasets using two types of AI models: specialised models designed for particular tasks and foundation models with broad application potential. Their computational experiments indicate that TRUECAM-wrapped models consistently outperformed their unwrapped counterparts in classification accuracy, robustness, interpretability, data efficiency and fairness. Prof. Zhang said, “TRUECAM strikes a sound balance between fully pathology AI-powered and purely pathologists-led cancer diagnosis. When the model’s confidence in its diagnostic outputs is high, the system can help handle clear-cut cases, while uncertain cases are flagged and passed on to pathologists for further review and clinical judgement. This AI–pathologist collaboration helps improve diagnostic efficiency, lighten pathologists’ workloads, enhance diagnostic reliability and scale up diagnostic capacity.” Prof. Zhang added, “While the published study focuses mainly on whole-slide images, we are further exploring additional modalities, such as molecular profiles including RNA-sequencing and diagnostic reports, to broaden the framework’s potential scope. TRUECAM provides a systematic solution to building trustworthy pathology AI and strengthens the foundation for deploying it in real-world settings.” This research has been published in the internationally renowned journal Nature Biomedical Engineering, and received funding support from the National Natural Science Foundation of China, the Research Grants Council of the Hong Kong Special Administrative Region, and the Shenzhen Science and Technology Program.

PolyU joined the "2026 Hong Kong Talents Beijing Tour" to explore new opportunities for Beijing-Hong Kong technology collaboration

Prof. YU Han, Assistant Professor of the Department of Chemistry at The Hong Kong Polytechnic University (PolyU), and Dr SUN Xiao-Hao, Research Assistant Professor of the Department of Civil and Environmental Engineering of PolyU, participated in the “2026 Hong Kong Talents Beijing Tour” from 10 to 15 August. The event was co-organised by the Beijing Overseas Scholars Centre and the Hong Kong Alumni Association of Beijing Universities.  The six-day programme aimed to deepen talent exchange and promote integrated development in education, technology, and innovation between Beijing and Hong Kong. During the tour, the delegation visited a wide range of innovation hubs across Beijing’s science and technology ecosystem, including national-level key laboratories, advanced R&D platforms, leading technology enterprises and innovation incubation centres.  Participants also participated in thematic exchanges and project matchmaking sessions, engaging with overseas innovation talent and exploring collaboration opportunities in areas such as artificial intelligence, frontier information technologies, new energy, advanced materials, and intelligent connected vehicles. In addition, the delegation toured major research and innovation facilities in Huairou, gaining first-hand exposure to national scientific infrastructure and frontier research developments. The visit to Zhongguancun Science Park further provided valuable insights into Beijing's innovation ecosystem, technology transfer mechanisms, and startup incubation environment. Through these exchanges, PolyU scholars strengthened connections with research and industry partners while identifying practical pathways for future Beijing–Hong Kong research collaboration and innovation partnerships. PolyU will continue to expand its cross-regional innovation network, creating new opportunities for technology collaboration and transforming shared research ideas into future innovation outcomes.

PolyU young researcher awarded FutureAM Breakthrough Award

Prof. Zibin Chen, Assistant Professor in the Department of Industrial and Systems Engineering at The Hong Kong Polytechnic University (PolyU), has been honoured with the FutureAM Breakthrough Award in recognition of his distinguished research in advanced manufacturing. The FutureAM Breakthrough Award is presented annually to recognise outstanding mid-career researchers in advanced manufacturing who demonstrate growing recognition for their contributions. The award was presented during the 2nd International Conference on Future of AM 2026 in Singapore, one of the largest international communities in advanced manufacturing. Prof. Chen’s primary research goal is to design sustainable materials that provide superior structural, functional and service properties, and to address practical engineering challenges across different material systems. His work on developing low-alloy, low-cost, and high-performance sustainable metal alloys, as well as record-breaking ferroelectric materials for energy storage and piezoelectric applications has attracted considerable attention worldwide. He has published numerous high-quality papers in top-tier journals such as Nature, Science, Nature Materials, Science Advances, Nature Communications, and Physical Review Letters. Recently, Prof. Chen pioneered two innovative strategies for advancing cryogenic materials, including the "Negative-Curvature Interface" strategy and "hierarchical nano-orderings (HNOs)" architecture. These strategies have tackled the classic strength-ductility-toughness trade-off and achieved record-breaking performance among all reported cryogenic alloys to date. Both innovations have been published consecutively in Nature Communications. In addition, he and his team have achieved a significant breakthrough in functional materials science, revealing and, for the first time, deliberately engineering microscopic “topological vortex” structures inside bulk piezoelectric crystals to dramatically boost their electromechanical performance. This groundbreaking discovery has been published in Nature Materials. Learn more about Prof. Chen’s research achievement: PolyU researchers collaboratively develop high-performance titanium alloys through additive manufacturing

PolyU develops AI-powered virtual patient simulation system integrating multimodal data to advance personalised cancer treatment

A research team at The Hong Kong Polytechnic University (PolyU) has developed a patient-centric “Artificial Intelligence (AI) Virtual Patient Simulation System”, overcoming the limitations of conventional static diagnosis. By dynamically integrating multimodal patient data, including genomic data, medical imaging and clinical records, the system creates a continuously updated “digital twin” model. It can not only track changes in a patient’s condition in real time, but also predict the potential effectiveness of different cancer treatment options, helping healthcare teams formulate more precise and personalised medical solutions. Other medical AI tools often rely on a single CT scan, genomic report or static clinical data for analysis, making it difficult to gain a comprehensive understanding of dynamic changes in a patient’s condition. Led by Prof. Lawrence CHAN, Associate Professor of the PolyU Department of Health Technology and Informatics, the team has developed the “AI Virtual Patient Simulation System”, based on a patient-centric digital twin platform. Combining a platform for healthcare professionals with a patient-facing mobile application, the system can conduct predictive analyses in response to real-time changes in a patient’s condition and simulate the effectiveness of different treatment options. It provides intelligent support for clinical diagnosis, condition monitoring and treatment assessment. The system is particularly suitable for cancer and critical care, where disease progression can be complex, treatment options diverse and medical costs high, injecting fresh impetus into the development of precision medicine. The system’s core strength lies in the close collaboration it enables between healthcare professionals and patients. The dedicated healthcare platform integrates multimodal data, including genomic data, medical imaging, pathology reports, laboratory test results and clinical records, helping doctors gain a comprehensive overview of a patient’s condition, enhance diagnostic and treatment decision-making, and streamline multidisciplinary consultations and referral processes. Meanwhile, the patient-facing mobile application enables patients to upload medical records, log daily symptoms, and track their health status. Through an encrypted Deep Feature QR code, medical data can be securely transferred across different clinics, hospitals and devices, enhancing data-sharing efficiency while safeguarding privacy. With the system, patients can shift from passively receiving treatment to actively participating in their health management, further strengthening doctor-patient collaboration. To advance the application of this technology in cancer care and treatment decision-making, the research team has introduced a clinical, data-driven, multi-scale AI framework for predicting immunotherapy response in patients with non-small cell lung cancer. The multimodal approach effectively integrates histopathological image features with clinical data, including gene expression profiles and cancer-type text. Named the Visual-Global Relation Fusion Network (ViGNet), the novel framework incorporates both a multi-scale visual encoder and a gene-driven encoder, enabling AI to analyse image and genomic features that are closely related to cancer treatment response. In qualitative and quantitative evaluations, ViGNet outperformed baseline approaches in response classification, achieving 82.55% discrimination performance in predicting immunotherapy response. This ground-breaking research enables more efficient integration of multi-source data and supports the practical deployment of AI methods in clinical settings, providing important insights for personalised treatment and clinical decision-making. The study has been published in the international journal Medical Image Analysis. Prof. Chan said, “The AI Virtual Patient Simulation System is an innovative and comprehensive platform that integrates diagnosis, monitoring and treatment assessment. In addition to identifying subtle yet crucial pathological connections across multimodal data, the system can also act as a ‘monitoring sentinel’, alerting healthcare teams when a patient’s biomarkers or symptoms show abnormalities. This transformative technology helps to shorten diagnosis and assessment times, supporting healthcare professionals in developing more precise, effective and personalised treatment plans for patients with cancer or other critical illnesses.”  This breakthrough research achievement was recently showcased at the Mobile World Congress 2026 in Barcelona, Spain, where, in recognition of its outstanding technical innovation, it was shortlisted as a finalist for the 2026 Global Mobile Awards in the category of Best Mobile Innovation for Connected Health and Wellbeing. This achievement demonstrates PolyU’s international influence in health technology and AI applications. The project has received funding from the PolyU Micro Fund and Seed Fund, as well as the GBA Innovation and Entrepreneurship Incubation Programme. It has also been conditionally accepted into the Hong Kong Science and Technology Park’s Incubation Programme and is now advancing into a new stage of commercialisation and industrialisation. As the system is steadily deployed in clinical settings, the real-world data it collects will inform drug development, clinical trials and treatment plan optimisation, further enhancing the medical innovation ecosystem and benefitting more patients.

PolyU develops wearable microneedle patch to empower conventional ultrasound for continuous glucose monitoring

Long-term diabetes management requires continuous blood glucose monitoring to detect abnormalities early and intervene in time. However, conventional fingertip blood sampling is both painful and inconvenient, while most continuous glucose monitoring (CGM) products currently available on the market rely on biological enzymes — requiring regular replacement, incurring higher costs, and demanding stringent storage conditions. A research team at The Hong Kong Polytechnic University (PolyU) has developed an enzyme-free, wearable, acoustically readable microneedle patch “ARMPatch”. Once applied to the skin, it can be read by a standard ultrasound probe to continuously reflect changes in blood glucose levels. The research was led by Prof. SU Zhongqing, Head of the Department of Mechanical Engineering and Chair Professor of Intelligent Structures and Systems at PolyU, together with Prof. MENG Long of the Shenzhen Institutes of Advanced Technology of the Chinese Academy of Sciences, Prof. Jae-Woong JEONG of the Korea Advanced Institute of Science and Technology and their research teams. The findings have been published in Science Advances, with Mr ZHANG Wanglinhan, a PhD student in PolyU’s Department of Mechanical Engineering, as the first author. Prof. Su said, “The ARMPatch that we have developed serves as a blood glucose monitoring accessory for any regular ultrasound probe, achieving minimally invasive, cost-effective, long-lasting, and stable continuous blood glucose monitoring. This approach offers a novel solution to enzyme-free CGM and expands the application of ultrasound technology in the fields of wearable biosensing and human health monitoring.” The working principle of the ARMPatch is simple. The patch is made from a glucose-responsive hydrogel based on phenylboronic acid (PBA). Once applied to the skin, its microneedles swell in response to blood glucose fluctuations. The higher the glucose level, the greater the swelling, and ultrasound detects these changes to determine glucose levels. This is precisely what makes the ARMPatch “acoustically readable”. The team conducted a series of in vitro and in vivo experiments. In vitro, the team demonstrated a good linear response across a glucose concentration range of 0–40 mM, sufficient to effectively monitor hyperglycemia, and delivered stable readings for up to 56 days — far outperforming enzyme-based devices that require frequent replacement. During in vivo tests, the degree of microneedle swelling read by ultrasound accurately reflected changes in blood glucose levels; the patch successfully monitored blood glucose for seven consecutive days on a freely moving nude mouse, remaining firmly attached and unaffected by the animal’s movement, with no inflammation or scarring on the skin after removal — confirming its biocompatibility and minimally invasive nature. Many people with diabetes already use portable or wearable ultrasound devices for long-term home monitoring of complications such as heart disease and kidney disease. For them, the ARMPatch requires no additional custom hardware — existing devices can be used to continuously monitor blood glucose. The team noted that, by adjusting the hydrogel composition, the same platform could in principle be extended to the continuous monitoring of various biomarkers such as pH, proteins, and bacteria. The team hopes to develop microneedle patches that are capable of monitoring multiple indicators simultaneously, turning wearable ultrasound devices into a one-stop tool for home health monitoring. Prof. Su added, “This study not only presents a novel wearable device that enables conventional ultrasound to be used for enzyme-free CGM, but also opens up a new avenue for monitoring diverse physiological information via standard ultrasound in a minimally invasive manner, using customised hydrogel microneedles.” This research was supported by the NSFC/RGC Joint Research Scheme along with additional funding from the Research Grants Council of Hong Kong, the National Natural Science Foundation of China, and the National Research Foundation of Korea. The PolyU University Research Facility in Materials Characterization and Device Fabrication and University Research Facility in Life Sciences also provided assistance.