Ministry Engineering Research Center
Overview：
Research Overview：
As a leading platform for technological innovation in China, the Health Intelligent Perception and Digital Twin Education Ministry Engineering Research Center has consistently been dedicated to driving breakthroughs in cutting-edge technologies and facilitating their application. The center aligns closely with the national science and technology development strategy, with a focus on intelligent perception, artificial intelligence, brain science, and health management. Upholding the philosophy of innovation-driven development, collaborative progress, and serving society, the platform is committed to establishing itself as a global leader in the field of health intelligent perception and digital twin technologies.
To date, the Health Intelligent Perception and Digital Twin Education Ministry Engineering Research Center has successfully undertaken more than 60 major scientific research projects, with total funding exceeding 100 million RMB. These projects span multiple national-level scientific research initiatives, including 3 National Key R&D Programs, 2 National Natural Science Foundation projects, the Ministry of Science and Technology's 2030 Science and Technology Innovation Plan for the New Generation of Artificial Intelligence, and the Pearl River Talent Plan among others. Through the successful implementation of these projects, the platform has not only driven the innovation of core technologies but has also accelerated the commercialization of its research outcomes. This is particularly evident in the fields of intelligent perception, emotional computing, and brain science, where significant advancements have been achieved.
The successful implementation of these research projects has not only enhanced the center's research capabilities in key technological areas but has also elevated China’s global competitiveness and influence in the field of technology. The platform’s continuous pursuit of technological innovation is pushing the boundaries of what is possible, and it has become a critical pillar in the realization of national science and technology strategies, as well as in driving industry technological upgrades. As a result, the center plays a crucial role in ensuring that China remains at the forefront of global technological competition.
Scientific Achievements：
Since its establishment, the Health Intelligent Perception and Digital Twin Education Ministry Engineering Research Center has made significant achievements in scientific research, leveraging its solid research foundation and robust technical accumulation. The center has excelled in academic publications, patent grants, and scientific awards, greatly advancing the development of related technological fields and providing substantial technical support for industrial upgrading.
The Health Intelligent Perception and Digital Twin Education Ministry Engineering Research Center has published over 1,500 academic papers in internationally recognized journals, including more than 1,100 SCI-indexed papers, with over 700 published in IEEE Transactions series journals. These papers cover a wide range of fields, including artificial intelligence, emotional computing, brain science, and intelligent manufacturing, significantly advancing these disciplines and having a profound impact on the academic community. The platform has received Best Paper Awards and over 80 papers have been listed as highly cited papers in the top 1% of Web of Science, further consolidating its leadership position in the academic world.
The center places a strong emphasis on technological innovation and intellectual property protection. To date, it has applied for a total of over 200 patents, covering a wide array of fields such as intelligent perception, emotional computing, EEG signal processing, and smart hardware. These patents not only strengthen the center's core competitiveness in technological innovation but also provide technological guarantees and application solutions for the development of related industries. Representative patents include the Multimodal Fusion Method Based on Normalized Mutual Information and the Edge Computing Method for Stacked Width Learning Systems, both of which have broad application prospects in smart healthcare, wearable devices, and other fields.
The research team at the Health Intelligent Perception and Digital Twin Education Ministry Engineering Research Center has consistently received prestigious domestic and international awards over the years, showcasing the platform's scientific strength and societal influence. The team has been honored with the Wu Wenjun Artificial Intelligence Award four times, the IEEE Systems Science's highest honor – the Norbert Wiener Award once, four first prizes and three second prizes in the Guangdong Province Science and Technology Progress Awards, as well as the Second Prize in the China Society of Image and Graphics Science and Technology Progress Award. These accolades not only recognize the exceptional innovation capabilities of the platform’s team but also provide strong support for its future research development and technology promotion.
The center’s research achievements span multiple cutting-edge technological fields, with particularly groundbreaking progress in intelligent manufacturing, artificial intelligence, and brain science, making it a key driver of technological innovation and industry advancement. These results not only support the implementation of national scientific and technological strategies but also provide core technological support for related industries, significantly promoting social and economic development as well as technological progress.
Technology Transfer：
From 2023 to 2024, centered around the theory of the Digital Parallel Human, the Engineering Center achieved comprehensive breakthroughs in achievement transformation within the field of mental health and psychological well-being.
In terms of technological innovation and R&D, the Center continued to promote technological upgrades based on its self-developed portable EEG headband device and intelligent drug rehabilitation APP. In 2023, the device and algorithms were initially applied. In 2024, the second-generation headband device achieved breakthroughs in lightweight design and multimodal data fusion, with battery life extended to 72 hours, supporting 5G real-time data transmission, and obtaining certification from the National Medical Products Administration. Concurrently, core technologies such as the sleep disorder monitoring algorithm were iteratively optimized, laying a solid foundation for subsequent applications.
Regarding application scenarios and implementation, the Center built a full-chain ecosystem covering university psychological screening - clinical diagnosis assistance - addiction intervention. In 2023, within the university setting, the portable headband completed screenings for 23,000 individuals across 10 institutions, increasing efficiency by 35% and reducing false detection rates by 22%. In the clinical field, collaborating with the Second Xiangya Hospital, an intelligent auxiliary diagnosis system for depression was developed, evaluating 8,000 cases with an accuracy rate of 89%. In drug rehabilitation, collaborating with compulsory isolation rehabilitation centers, graded assessments were implemented for over 2,000 individuals. In 2024, the technology deepened further: the headband assisted in diagnoses for 12,000 cases across 12 medical institutions, with clinical accuracy rising to 92%; the intelligent drug rehabilitation APP was upgraded to version 2.0, serving 18,000 individuals, with relapse risk prediction accuracy reaching 88%, and its application scope expanded to Guangdong, Gansu, and other regions.
Detail
Name：
The Health Intelligent Perception and Digital Twin Engineering Research Center of the Ministry of Education
Introduction：
The Engineering Research Center of the Ministry of Education for Health Intelligent Perception and Digital Parallel Human (hereinafter referred to as the Center) was officially approved for construction by the Ministry of Education in 2022, with South China University of Technology as the host institution. The Center brings together strengths from multiple disciplines such as Computer Science and Technology, Information and Communication Engineering, Electronic Science and Technology, and Biomedical Engineering, aiming to create a high-level R&D and engineering platform addressing major national needs and international academic frontiers in the field of artificial intelligence.
The core positioning of the Center is to conduct applied basic research and tackle key technological challenges in the fields of health intelligent perception and digital parallel human, committed to achieving technological breakthroughs, achievement transformation, and the cultivation of high-end talent. In terms of discipline construction, the Center strongly promotes the integration of Medical-Engineering interdisciplinary studies. By undertaking national-level major research projects and producing high-level academic achievements, it effectively supports the development of related advantageous disciplines at South China University of Technology and fosters the formation of new interdisciplinary fields. In scientific research innovation, the Center focuses on four main research directions: Intelligent Perception and Communication, Intelligent Information Transmission, Fundamental AI Algorithms, and Full-Life-Cycle Digital Parallel Human, aiming to solve bottleneck technical problems in China's AI fundamental theory and cognitive models, and to strengthen national strategic scientific and technological capabilities. In talent cultivation, the Center focuses on training innovative, practical high-end talents with multidisciplinary backgrounds. Through project experience, industry-university joint training, and other models, it has supplied a group of influential experts and interdisciplinary graduate students in the fields of chips, intelligent perception, and digital parallel human.
The Center's short-term (acceptance period) construction goals are to overcome key core technologies in intelligent perception, AI algorithms, and digital parallel human, build a digital parallel human platform, and conduct demonstration applications in scenarios such as emotional/mental health and addiction/sleep monitoring, forming scalable engineering solutions, while also introducing and cultivating a well-structured, high-level talent team. Its medium to long-term goal by 2030 is to reach internationally advanced or leading levels in related fields, build a complete full-life-cycle digital parallel human platform and industrial ecosystem, lead the transformation and upgrading of the health industry, and ultimately establish the Center as a first-class innovation strategic highland and high-end talent cultivation base with significant international influence, serving the national strategy of leveraging science and technology to lead economic development.
Research direction：
The 14th Five-Year Plan of China outlines a strategic focus on cutting-edge fields such as artificial intelligence, integrated circuits, healthcare, and brain science, aiming to implement a series of forward-looking, strategic national major science and technology projects. The Health Intelligent Perception and Digital Twin Engineering Research Center of the Ministry of Education addresses the major national needs in the field of artificial intelligence and the international academic frontiers. The center conducts key technological research on the common and applied scientific foundations of health intelligent perception, with an emphasis on enhancing perception and interaction, as well as the integration of cognition with environments/scenarios in strong AI systems.
The center focuses on developing universal and specialized algorithms for intelligent perception and cognition, based on the results of these studies, while exploring transformative new methods, models, systems, and platforms. This work aims to lead and support China's scientific and technological innovation in the field of artificial intelligence and its sustainable industrial development, as shown in the overall framework in Figure 1.
Figure 1 Overall Architecture
The research tasks of the Health Intelligent Perception and Digital Twin Engineering Research Center of the Ministry of Education are illustrated in Figure 2. The center will focus on intelligent perception technologies, studying complex and diverse modal information obtained from wearable devices, external sensing devices, and crowd-intelligence sensing equipment, as well as high-reliability communication front-end technologies. The research will also explore efficient intelligent transmission technologies, with a key focus on the development of basic algorithms for intelligent cognition based on shallow/deep networks, as well as brain-like intelligence theories that simulate the brain’s perception and cognitive mechanisms.
Furthermore, the center will develop an intelligent perception and cognition algorithm library that integrates various novel algorithms. It will build a comprehensive digital twin system covering the entire lifecycle, enabling precise and all-encompassing management of human health across various dimensions and stages. The relevant theoretical technologies will be applied in multiple demonstration applications, such as emotional and mental health assessment, addiction treatment, and sleep health management.
Upholding the philosophy of theoretical breakthroughs, technological innovation, and application deployment, the center will conduct foundational and applied research in areas such as intelligent perception devices, smart information transmission technologies, AI foundational algorithms and cognitive theories, full-lifecycle digital twins, as well as demonstration centers and application scenarios. The research aims to serve the entire big health industry chain, driving innovation and development across related industries.
Figure 2 Research Tasks
1.1 Intelligent Perception and Communication Front-End Technology
Intelligent perception and communication front-end technologies constitute the foundational stage of the platform’s construction, serving as the critical cornerstone for health data acquisition, intelligent transmission, and the realization of smart healthcare. This research direction focuses on two major domains: wearable sensors and high-reliability communication front-end technologies, with an emphasis on a full-stack layout spanning materials, devices, signal processing, and transmission protocols. Through this line of research, a comprehensive technological system covering perception–interaction–transmission will be progressively established, laying a solid foundation for smart health, brain-inspired intelligence, and frontier applications of artificial intelligence.
In terms of wearable sensors, the research emphasizes overcoming the inherent limitations of current signal acquisition and processing. Existing sensors primarily capture human motion, physiological signals, and environmental information; however, their outputs often exhibit non-sparse characteristics. This poses a challenge for traditional compressive sensing algorithms, which are unable to guarantee real-time performance and struggle to meet large-scale application demands. To address this, the center will develop novel real-time reconstructive compressive sensing algorithms, leveraging spatiotemporal redundancy to achieve more consistent and accurate representations of sensing targets. Specifically, for EEG acquisition, the research will address key challenges associated with portable dry electrodes, such as high impedance, insufficient anti-interference capabilities, and discomfort during prolonged wear. The study will systematically optimize electrode structures and materials, design active circuits to reduce impedance, balance surface pressure, and employ filtering alongside Independent Component Analysis (ICA) for comprehensive noise and artifact removal. The ultimate goal is to deliver wearable EEG devices that are both comfortable and portable while offering high-fidelity signal quality, thus advancing mental health monitoring and brain-inspired intelligence research from laboratory validation to large-scale deployment.
Figure 3 Wearable Device Data Sensing Technology
On the communication front-end, the research targets the challenges brought about by the explosive growth of sensing nodes and wearable devices. With the rapid proliferation of multimodal sensing technologies, enabling efficient data transmission under conditions of high concurrency and low latency has become paramount. The research will include: high-reliability signal modulation and coding techniques to ensure adaptability and robustness of multimodal data across diverse scenarios; multiplexing and multi-user access methods to support efficient sharing across multiple terminals and users; channel coding and error-correction mechanisms to enhance fault tolerance and stability; and advanced digital signal processing techniques for complex environments to improve signal quality, extract key information, and reduce redundancy. By combining multimodal information collected from devices such as cameras, microphones, temperature sensors, and accelerometers with these cutting-edge techniques, the center will establish a complete communication front-end system covering acquisition, compression, transmission, and storage. The objective is to build an intelligent communication framework that not only provides high bandwidth and low latency but also ensures strong security and scalability.
The significance of this research lies in its ability to provide high-precision foundational data support for high-value applications such as mental health monitoring, sleep quality assessment, and addiction state detection, while exerting a profound impact on domains including smart healthcare, public health, and intelligent elderly care. By establishing an independently controllable wearable perception and communication front-end technology ecosystem, China will secure a strategic high ground in the intersection of smart health and artificial intelligence. Meanwhile, the research will drive iterative upgrades of domestic core components and communication technologies, overcoming critical bottlenecks and forming a closed-loop innovation cycle from devices to algorithms, and from protocols to applications. In the future, the research is expected to yield demonstrative achievements in scenarios such as intelligent diagnosis of mental disorders, personalized health management, home-based rehabilitation monitoring, and public health emergency response—ultimately propelling the coordinated upgrading and leapfrog development of the entire health industry chain.
1.2 Intelligent Information Transmission Technology
Intelligent Information Transmission Technology serves as a crucial connecting link that bridges the upper and lower layers in the smart healthcare system. Its primary mission is to establish unobstructed data channels from the perception layer to the cloud layer, enabling the fast, accurate, and secure transmission of multi-source heterogeneous health data. Under conditions of limited resources and complex environments, ensuring the stable and efficient flow of health data across different modalities and dimensions constitutes a core challenge in constructing intelligent information systems. This research will focus on four key areas—edge-side transmission, cloud-edge collaboration, textile computing, and cutting-edge millimeter-wave communication technology—to comprehensively enhance information transmission efficiency from multiple perspectives and build a next-generation communication foundation for smart healthcare.
In the realm of intelligent transmission of health data, the research prioritizes addressing the transmission bottlenecks arising from variations in the number of acquisition channels, sampling frequencies, and encoding modes for diverse physiological signals such as electroencephalography (EEG), electrocardiography (ECG), electrooculography (EOG), posture, body temperature, and respiratory rate. By introducing novel electromagnetic energy transmission methods and wide-band conversion techniques, and designing collaborative transmission mechanisms, a high-speed channel between edge terminals and the cloud is established. This ensures efficient data transmission even under complex environments and limited resource constraints, with the goal of supporting a real-time responsive intelligent health information system and providing reliable data sources for downstream algorithms and applications.
Regarding cloud-edge collaboration and transmission, the research focuses on constructing an efficient collaborative mechanism between edge computing and cloud computing. Through dynamic multi-link planning, intelligent address allocation, and task decomposition algorithms, data transmission paths and resource allocation are optimized to enhance the flexibility of cloud-edge data transmission and the response speed of cloud-based processing. Simultaneously, part of the storage and computing tasks are migrated to edge devices closer to users, reducing redundant transmission and improving the real-time performance of data processing and overall system efficiency. The objective is to build a collaborative transmission architecture characterized by light terminals—strong edges—intelligent cloud, ensuring smoother circulation of health data throughout the entire link and providing real-time support for personalized healthcare services.
In the field of textile computing and unobtrusive interaction, the research aims to break through the limitations of traditional Internet of Things (IoT) by exploring novel textile computing technologies that integrate sensors and conductive materials into textiles. Such devices enable unobtrusive data collection and interaction in daily wearable scenarios but also face multiple challenges related to transmission distance, bandwidth, and security. The research will focus on developing next-generation transmission protocols and encryption mechanisms to improve the transmission speed, stability, and privacy protection capabilities of textile computing. The goal is to create a more intelligent, convenient, and secure human-machine interaction experience, promoting its application in medical monitoring, rehabilitation training, and home-based health management.
For efficient millimeter-wave transmission, the research will target the communication requirements of post-5G and future 6G networks, exploring the integrated application of millimeter-wave and C-band technologies. Millimeter-wave technology offers advantages of high bandwidth and low latency, which can significantly enhance the data-carrying capacity of a single link. This research will develop millimeter-wave transceiver, frequency synthesis, and mixing technologies integrated with C-band, construct a dual-band integrated radio frequency architecture, and integrate it into novel communication chips and high-gain antenna arrays. The objective is to break through the bottlenecks in health data transmission, achieve low-latency, high-rate, and high-stability intelligent medical communication, and lay a technical foundation for the expanded application of future smart healthcare and the full-information industry.
Overall, the research on Intelligent Information Transmission Technology not only addresses issues of data transmission efficiency, real-time performance, and security at the technical level but also provides solid support for smart medical care, public health monitoring, and intelligent elderly care at the application level. Through the in-depth integration of edge-side technologies, cloud technologies, and cutting-edge communication technologies, China will achieve leapfrog breakthroughs in the fields of health data transmission and intelligent medical communication, form an independent and controllable core technology system, and gain a leading position in international competition.
1.3 Fundamental Algorithms and Cognitive Theories of Artificial Intelligence
The development of artificial intelligence (AI) is generally divided into three phases: computational intelligence, perceptual intelligence, and cognitive intelligence. Early-stage AI primarily relied on high-speed computing and large-scale storage; in the current era of big data, AI is centered on perceptual intelligence, enabling machines to gradually acquire perceptual capabilities such as vision, hearing, and touch. With the continuous advancement of brain-inspired science and cognitive science, AI is now moving toward a new phase of cognitive intelligence, whose core objective is to enable machines to understand, reason, and make sound judgments in complex environments, much like humans do. Cognitive intelligence is not only an inevitable direction for the evolution of AI technology but also a core pathway to realizing autonomous intelligent systems and strong AI in the future.
The focus of this research direction is to endow machines with higher-level cognitive capabilities, enabling them to perform reasonable reasoning, association, and decision-making in complex, dynamic, and multi-modal scenarios. The research will revolve around semantic understanding, associative reasoning, autonomous learning, and cross-modal cognition. While striving to improve the computing speed, storage efficiency, and energy consumption optimization of existing algorithms, it also aims to construct an innovative brain-inspired computing framework based on the cognitive functions of the human brain. Through this exploration, AI will transition from imitation at the perceptual level to simulation at the cognitive level, gradually achieving a leap from efficient assistance to autonomous intelligence.
At the algorithm level, the research will first focus on the integration of shallow networks and deep networks, exploring an innovative deep-wide integration framework. Traditional deep networks, while performing excellently in structured data such as speech, images, and videos, suffer from problems such as large parameter sizes, overfitting, and high computational power consumption. In contrast, broad learning systems (BLS) possess the advantage of fast feature generation but lack hierarchical structure. By organically integrating these two types of networks and utilizing stacked and incremental training methods, a deep-wide integrated system with multi-dimensional cognitive functions—including learning, memory, reasoning, and judgment—is formed. This system not only enables rapid model reconstruction but also simulates the vertical and horizontal connection mechanisms between different regions of the human brain, thereby significantly enhancing the cognitive expression capability and modeling efficiency of the algorithm. Meanwhile, the research will delve into emerging models such as graph convolutional neural networks (GCNs), breaking through the limitation that traditional convolutional networks struggle to process non-Euclidean geometric topological data and enabling AI to better simulate the complex organizational structure and information processing mechanisms of the human brain.
Figure 4 Schematic Diagram of the Innovative Theoretical Algorithm for Deep-Wide Integration
In terms of brain-inspired algorithms, the research will draw inspiration from the information processing and cognitive mechanisms of the human brain to explore new algorithm paradigms such as transfer learning, meta-learning, and temporal learning. By simulating the neuronal connection and association mechanisms of the human brain across different temporal and spatial scales, a brain-inspired computing framework with adaptive and self-evolving capabilities is established. This framework not only supports efficient learning with a small number of samples but also dynamically and incrementally adjusts feature layers and internal connections, thereby achieving multi-task collaboration and horizontal transfer learning capabilities. The goal is to drive machines to gradually approach the level of the human brain in areas such as autonomous learning, reasoning and decision-making, and human-machine interaction.
Simultaneously, this research direction will also focus on conducting theoretical and methodological research on crowdsensing, swarm intelligence, and autonomous intelligence. By integrating the perceptual capabilities of large-scale individual devices (e.g., mobile phones, wearable devices), the research explores the emergence mechanisms, evolutionary laws, and collaboration methods of swarm intelligence, forming a swarm intelligence system capable of completing large-scale complex tasks. On this basis, it further explores the development path of autonomous intelligence, promoting breakthroughs in key technologies such as zero-shot learning, adversarial learning, and unmanned autonomous systems, and providing verification for complex scenarios such as unmanned aerial vehicles (UAVs), robot swarms, and group collaboration.
Figure 5 New Cognitive System Brain-Inspired Computing Framework
The significance of this research direction lies not only in promoting algorithmic innovation and theoretical breakthroughs in AI but also in realizing the leap of AI from perception to cognition, significantly enhancing the autonomy and adaptability of machines in complex tasks. Its outcomes will be widely applied in fields such as smart healthcare, brain science research, intelligent manufacturing, group collaboration, and national security, providing strong technical support for China to gain a leading position in the international competition in cognitive intelligence and strong AI.
1.4 Full Lifecycle Digital Twin
With the development of the economy and the improvement of people's living standards, society's attention to physical health and mental health has been continuously increasing. However, China currently still faces the contradiction between the shortage and uneven distribution of medical resources, making it difficult to fully meet the multi-level health needs of the public. Against this backdrop, promoting the in-depth integration of artificial intelligence (AI), big data, and healthcare, and exploring the innovative application of digital twin technology in healthcare have become crucial approaches to realizing the digital transformation of medical services and enhancing the capacity of public health services. This research direction focuses on the construction and application of the full-lifecycle digital parallel human—that is, by integrating virtual and physical elements, a digital mapping model covering all elements, dimensions, and cycles of an individual is established to drive the development of precision medicine, preventive medicine, and intelligent health management.
Figure 6 New Cognitive System Brain-Inspired Computing Framework
A health data collection system incorporating multiple types of sensors (including contact and implantable sensors) will be developed to break down information silos and achieve interconnection and intercommunication of multi-source heterogeneous data such as physiological data, medical history, and medication history. On this basis, a high-fidelity virtual model with multi-dimensional and multi-spatiotemporal scales will be built to ensure real-time consistency between the digital parallel human and the actual human body. Through machine learning, deep anomaly detection, and visual analysis, the correlations and causal relationships between various factors and diseases will be explored, and an interpretable knowledge base will be formed. This enables the rapid capture and feedback of easily overlooked early physiological signals of diseases, thereby advancing the precision of disease prediction, prognosis analysis, and health intervention.
Finally, in terms of data-model iterative interaction and dynamic evolution, this research will drive the digital parallel human to evolve from an initial prototype to a virtual entity with individual-specific significance and dynamic evolutionary capabilities. By leveraging decoupled learning, causal reasoning, and Bayesian modeling, the discrepancies between the digital parallel human and the actual human body will be corrected and updated, allowing the virtual model to be continuously revised and improved over time. This not only enables personalized and precise health management but also provides doctors and patients with scientifically reliable health intervention plans through virtual-physical interaction.
The research on the full-lifecycle digital parallel human holds great significance. It not only provides individuals with personalized health management and medical services covering the entire lifecycle but also plays a role in public health governance, facilitating the optimal allocation of national medical resources. Through the integration of virtual twins and AI, China is expected to take a leading position in the next-generation innovative technologies for intelligent healthcare, promoting the transformation of healthcare services toward proactive prevention, precise intervention, and intelligent management, and providing strategic technical support for building a Healthy China.
1.5 Demonstration Centers and Application Scenarios
The construction of demonstration centers and application scenarios is a key link in the transformation and implementation of the platform's research outcomes. By focusing on four typical directions—mental health, addiction and sleep, personalized health management, and elderly care and disability assistance—this research aims to deeply integrate cutting-edge technologies with real-world needs, form a benchmark smart health application system, and promote the comprehensive popularization of advanced theories and technologies at the social level.
In the demonstration application of emotional and mental health, the research focuses on exploring a new model integrating brain science and AI, and constructing an intelligent platform that integrates diagnosis, monitoring, and services. Multi-modal sensors are used to collect brain signals, behavioral, and emotional data; combined with deep-wide neural networks and classification models, early identification of mental illnesses is achieved, reducing subjective errors in diagnosis. At the intelligent monitoring level, the key task is to develop disease risk prediction and trend analysis models, using deep learning methods to explore potential etiologies and early symptoms, and establishing a hierarchical health assessment and early warning system. At the service level, the research will construct a mental health service platform through brain disease correlation mining and personalized recommendation algorithms, providing diagnostic assistance for doctors and generating personalized reports and management plans for users, thereby promoting the transformation of mental health services from passive treatment to proactive intervention. Its significance lies in providing new tools for the early diagnosis and treatment of mental illnesses and improving the overall mental health level of society.
Figure 7 Center's Research Program on Emotion and Mental Health
In the demonstration application of addiction and healthy sleep, the research goal is to establish an intelligent sleep health and brain science service system. By collecting and fusing multi-modal data (such as brain waves, brain imaging, behavior, and environment), the key factors affecting sleep quality are analyzed, and a healthy sleep factor correlation model is established using graph neural networks and deep-wide learning algorithms. At the intelligent monitoring level, the research will collect and analyze sleep data in real time, constructing a precise sleep state classification and hierarchical assessment system. At the intelligent service level, through personalized sleep profiles and visual monitoring platforms, users will be provided with scientific sleep improvement suggestions. Meanwhile, for drug addicts, the research will combine transfer learning and semi-supervised learning algorithms to establish an intelligent assessment model for addiction status, providing support for risk monitoring during drug detoxification and relapse prevention. The significance of this direction not only lies in improving the overall sleep health level of the public but also in providing scientific means for drug detoxification and rehabilitation, contributing to public safety and social governance.
Figure 8 Center's Research Program on Addiction and Sleep Health
In personalized parallel human health management, the research focuses on relying on the digital parallel human service platform to construct a personal health big data repository, realizing the sharing and collaboration of health data among hospitals, communities, and individuals. Wearable devices are used to collect physiological status and lifestyle data in real time; combined with historical medical records and environmental information, a personalized virtual model of the digital parallel human is established. The goal is to realize real-time prediction and early warning of health risks driven by data, assist doctors in precise diagnosis and treatment, and at the same time, through real-time interaction between users and their parallel humans, continuously iterate and revise the evolutionary model to ensure the comprehensiveness and personalization of health management. Its significance lies in promoting the transformation from disease-centered to people-centered healthcare, realizing full-lifecycle smart health management.
In elderly care and disability assistance services, the research will focus on the major needs of an aging society, developing an intelligent health monitoring system based on flexible wearable sensors and multi-modal data analysis. The key task is to collect long-term data (such as EEG, ECG, respiration, and electrodermal activity) and combine audio-visual behavioral information in virtual reality tasks to realize auxiliary diagnosis of mental illnesses and intelligent monitoring of chronic diseases. Meanwhile, the research will build a smart health service platform for diabetes, eye diseases, and elderly health monitoring, and conduct in-depth cooperation with medical institutions and relevant enterprises to promote the demonstration application of elderly care and disability assistance technologies. Its significance lies in alleviating the pressure caused by insufficient medical resources and population aging, and providing high-quality and inclusive smart health services for the elderly and people with disabilities.
To sum up, demonstration centers and application scenarios are not only important windows for the transformation of technological achievements but also strategic drivers for promoting the in-depth integration of smart healthcare and social health services. Through research and application in this direction, China is expected to form a number of promotable innovative models in fields such as mental health, sleep health, personalized health management, and elderly care and disability assistance, enhance the level of public health governance, and advance the realization of the strategic goal of building a Healthy China.
Talent team：
Team Overview
The Health Intelligent Perception and Digital Twin Engineering Research Center of the Ministry of Education, relying on a robust talent pool, has built a high-level research team encompassing multiple disciplines, including computer science, artificial intelligence, brain science, and data science. Currently, the center employs over 110 fixed staff members, including researchers, engineers, and management personnel, as well as technical support personnel. More than 80% of the team holds advanced academic qualifications, including Master's and Doctorate degrees.
The center's core research force is particularly outstanding. A total of 14 individuals in the team have received national-level talent recognition, including National Outstanding Youth Fund recipients, Changjiang Scholars, and other top-tier talents. In addition, 10 individuals have been honored with provincial and ministerial-level talent titles. These high-level talents have played a pivotal role in the center's scientific research projects, driving technological innovation and providing strong support for the successful execution of major research projects.
The team structure is well-organized, with a comprehensive academic title system. A significant portion of the team holds senior titles, ensuring the platform’s stability and innovation capabilities in various research endeavors. At the same time, the platform actively attracts and nurtures young academic talents, providing a continuous source of innovative power for the team’s long-term development.
Director of the Center
The center is led by Professor C. L. Philip Chen. Dr. Chen is a Chair Professor and Doctoral Supervisor at South China University of Technology, and the Dean of the School of Computer Science and Engineering. He is an elected Foreign Member of the European Academy of Sciences, an Academician of the European Academy of Sciences and Arts, and the Vice President of the Chinese Association for Automation. He is also an expert under the High-Level Foreign Talent Introduction Program.
Dr. Chen has previously held prominent academic positions, including Chair Professor and Dean at the Faculty of Science and Technology, University of Macau, and a tenured Professor at the University of Texas at Austin, where he also served as Associate Dean of the College of Engineering and Head of the Department of Electrical and Computer Engineering.
Professor Chen is an IEEE Fellow, an AAAS Fellow, an IAPR Fellow, and a Fellow of the Chinese Association for Automation, as well as a Fellow of the Hong Kong Engineers Association. He currently serves as the Associate Editor of prestigious IEEE journals such as IEEE Transactions on AI and IEEE Transactions on Fuzzy Systems. He was the Editor-in-Chief of the IEEE Transactions on Systems, Man, and Cybernetics: Systems journal from 2013 to 2019, and the IEEE Transactions on Cybernetics journal from 2020 to 2021. He served as the International President of the IEEE Systems, Man, and Cybernetics Society (SMCS) from 2012 to 2013, and he was Chair of the IEEE SMC Society’s IEEE Fellow Evaluation Committee, Executive Director, and Distinguished Lecturer.
Professor Chen has published over 800 papers in international journals, including more than 500 in IEEE Transactions. He has won numerous Best Paper Awards, including the IEEE Transactions on Neural Networks and Learning Systems Best Paper of the Year Award in both 2018 and 2021, as well as the Franklin Taylor Best Paper Award at the IEEE International Conference on Systems, Man, and Cybernetics in 2019. His work has received over 37,000 citations according to Google Scholar and over 23,000 citations in Web of Science. He currently has 60 papers listed among the top 1% of highly cited papers in Web of Science, three of which are in the top 0.1%. He holds four U.S. patents and has published an academic monograph. His contributions to intelligent systems and control, brain-inspired intelligence, computational intelligence, and data science are widely recognized.
Professor Chen has received five IEEE Outstanding Contribution Awards and has been a reviewer for ABET (Accreditation Board for Engineering and Technology). He is also a member of China’s Computer Science Academic Steering Committee. He made significant contributions to the engineering education of Macau, including establishing the Macau University of Science and Technology’s Zhuhai Research Institute in 2013, where he served as Director. During his tenure, the research institute secured over 30 National Natural Science Foundation projects. In 2016, he received the Outstanding Electrical and Computer Engineering Award from his alma mater, Purdue University. In 2018, he was honored with the IEEE Norbert Wiener Award, the highest academic award in systems and human-machine control theory, and in 2021, he was awarded the IEEE Joseph G. Wohl Lifetime Achievement Award. Furthermore, he has been named a Highly Cited Researcher by Clarivate Analytics for four consecutive years from 2018 to 2021.
Outstanding Young Talent
The center boasts a high-level, solid, and well-structured scientific research team, with several prominent young talents recognized in both national and international academic circles. These include recipients of prestigious awards such as the Ministry of Education’s New Century Talent Program, the Young Thousand Talents Program, and Guangdong Province’s Excellent Young Talent Program. These interdisciplinary talents play a key role in the center’s research activities and contribute complementary strengths to the team’s research objectives.
Xue Quan, a Professor and Doctoral Supervisor, is an overseas high-level talent introduced in 2011 and an IEEE Fellow. He currently serves as the Dean of the School of Electronics and Information Engineering, and the Dean of the School of Microelectronics at South China University of Technology. He is also the Director of the Guangdong Provincial Key Laboratory of Millimeter-Wave and Terahertz Technology, a member of the National 6G Technology Research and Development Expert Group (the only member from a university in South China), and the Chairman of the Guangdong Provincial Communications Standardization Technical Committee. Xue has published over 400 SCI papers and holds more than 50 patents, including 5 transferred to U.S. companies.
Che Wenquan, a Professor and IEEE Fellow, serves as the Deputy Director of the Guangdong Provincial Key Laboratory of Millimeter-Wave and Terahertz Technology. He has received numerous prestigious awards, including the National Outstanding Youth Science Fund, the Defense Science and Technology Progress Award, and the Guangdong Provincial Science and Technology Award. Che has published over 300 SCI/EI papers and holds more than 30 patents.
Chen Min, a Professor and Doctoral Supervisor, is an IEEE Fellow and IET Fellow, with more than 39,500 citations on Google Scholar and an H-index of 94. He has published over 200 papers in renowned international journals and conferences and has authored 12 books. Chen’s work has earned him multiple awards, including the IEEE Fred W. Ellersick Prize and the IEEE Jack Neubauer Memorial Award. He is a guest editor for numerous international journals, focusing on areas such as 5G/6G, AI in healthcare, mobile cloud computing, and communication networks.
Chen Weineng, a Professor and Doctoral Supervisor, is a recipient of the National Outstanding Youth Science Fund, the Guangdong Province Natural Science Outstanding Youth Fund, and the Guangzhou Pearl River New Star Talent Program. He specializes in Computational Intelligence and Evolutionary Algorithms, and his research has made significant contributions to these fields.
Tong Zhang is currently a professor, doctoral supervisor, and Associate Dean of the School of Computer Science and Engineering, South China University of Technology. He is the recipient of the National Natural Science Fund for Excellent Young Scholars and the Guangdong Natural Science Fund for Distinguished Young Scholars, and was listed among the 2024 world’s top 2% scientists. His research interests lie in the field of fundamental studies and practical applications of artificial intelligence, including affective computing and large language models. He has led over 20 projects, including projects funded by the National Natural Science Foundation of China or the Natural Science Foundation of Guangdong Province, and collaboration projects with enterprises. He has published over 170 academic papers, of which 10 are highly cited papers. He has also filed over 100 patents, including 2 PCT patents, with 30 patents granted. He is the Associate Editor of IEEE Transactions on Affective Computing, IEEE Transactions on Computational Social Systems, and Journal of Intelligent Manufacturing, all of which are JCR Q1 journals. Dr. Zhang is the Deputy Director of the Computational Social and Social Intelligence Committee of the Chinese Association of Automation, and the Deputy Secretary-General of the Youth Working Committee. He is also the Deputy Director of the Engineering Research Center of the Ministry of Education on Health Intelligent Perception and Paralleled Digital-Human, and the Deputy Director of the Guangdong Provincial Key Laboratory of Computational AI Models and Cognitive Intelligence. He has received the First Prize of the Science and Technology Progress Award of Guangdong Province, the 10th Wu Wenjun AI Outstanding Youth Award, the ACM Guangzhou Rising Star Award, and the Franklin V. Taylor Best Paper Award from the IEEE Systems, Man, and Cybernetics Society. He has also guided students to win five National Gold Awards in the finals of the China International College Students’ “Internet+” Innovation and Entrepreneurship Competition.
The center also employs several other excellent scientific researchers, technical support staff, and management personnel, such as Xu Xuemiao, Liu Fagui, Li Bin, Yang Chengguang, Huang Han, Mao Aihua, Lin Weiwei, Nie Yongwei, and Wang Xiumin. South China University of Technology, the center's affiliated institution, places high importance on the center’s development and has established a leadership team to oversee and promote the center's growth. The center is also supported by dedicated research assistants, financial assistants, and engineering technical staff.
Overall, the center has formed a large, well-structured, interdisciplinary, and highly internationalized talent team. The center currently employs over 110 fixed and technical support staff, with 14 individuals having been recognized as part of national-level talent programs and 10 others receiving provincial or ministerial-level recognition. The team includes professors, researchers, associate professors, postdoctoral researchers, and young scholars at various levels, with a high proportion of staff holding doctoral degrees. The center’s research and engineering capabilities are robust, ensuring its continued growth and innovation.
Under the leadership of core academic figures and young research leaders, the platform emphasizes the development of a sustainable talent pipeline. It actively attracts top-tier domestic and international scientific leaders and fosters graduate talents with an interdisciplinary background in medicine-engineering. The center’s talent team is progressively becoming more diverse and well-rounded, ensuring a balanced age structure and a wide distribution of expertise. Through flexible “fixed + fluid, long-term + short-term, full-time + part-time” mechanisms, the center effectively attracts and integrates talent from top universities, research institutes, and industries worldwide.
The platform is dedicated to fostering an open, collaborative, and innovative talent ecosystem through a well-developed talent training system, evaluation mechanisms, and incentive structures.
The aim is to strengthen the center’s competitive edge in the fields of Health Intelligent Perception and Digital Twin technologies, ensuring continuous high-level research outputs, facilitating technology transfer, and ultimately establishing itself as a world-class talent hub and innovation center.
Projects：
As a leading platform for technological innovation in China, the Health Intelligent Perception and Digital Twin Engineering Research Center of the Ministry of Education focuses on advancing cutting-edge technologies and achieving significant breakthroughs in scientific research. The center actively undertakes a variety of major national and provincial-level research projects. To date, the platform has successfully undertaken more than 20 major scientific projects with a total funding of over 20 million RMB. Through these projects, the platform has made significant progress in multiple fields, particularly in high-tech areas such as intelligent manufacturing, artificial intelligence, and computational brain science, showcasing its strong disciplinary advantages and technological accumulation.
Below are some of the representative research projects undertaken by the platform:
1. National Key R&D ProgramProject Title: Theory and Methods of Non-deterministic Manufacturing Big Data for Flexible SystemsThis project aims to address the non-deterministic challenges in manufacturing processes within flexible systems, exploring the deep integration of big data and advanced manufacturing technologies to promote core technological innovations in the field of intelligent manufacturing and support the high-quality development of China’s manufacturing industry.
2. National Key R&D ProgramProject Title: RF Chip Technology Integrating C-band and Millimeter-wave TechnologiesThis project focuses on the cutting-edge field of RF chip technology, integrating C-band and millimeter-wave technologies. It aims to conduct in-depth research and overcome key challenges in the design of high-frequency, wideband RF chips, driving major applications in communications, radar, and electronic countermeasures.
3. Ministry of Science and Technology 2030 Innovation ProgramProject Title: Mechanisms of Collective Intelligence Emergence and Evolutionary Computational MethodsThis project explores the essence of collective intelligence and emergent phenomena, investigating the application of evolutionary computational methods in intelligent systems. By studying computational models, the project aims to understand the dynamics of group behavior and large-scale complex system evolution, promoting innovations in artificial intelligence theory and technology.
4. National Natural Science Foundation – Excellent Young Scientist ProgramProject Title: Particle Swarm Optimization AlgorithmsThis project focuses on the fundamental research and optimization applications of particle swarm optimization algorithms, exploring new methods for solving multi-objective optimization and high-dimensional complex problems. It aims to provide new solutions for intelligent computing and data analysis.
5. National Key R&D Program – Key Scientific Issues of Revolutionary Technologies Sub-ProjectProject Title: Early Detection and Intervention Methods for Depression Disorders Based on Psychophysiological Multimodal InformationThis sub-project explores the application of psychophysiological multimodal information in the early identification and intervention of depression disorders. By combining EEG, ECG, eye movement, and other physiological signals, the project aims to develop new methods for diagnosing and intervening in depression, thus advancing the development of intelligent medicine and mental health technologies.
6. “Pearl River Talent Program” Innovation and Entrepreneurship TeamTeam Name: Computational Brain Science and Emotional Intelligence TeamThis team focuses on foundational research and technological innovations in computational brain science and emotional intelligence. The team is dedicated to advancing the integration of brain information processing mechanisms with emotional intelligence technologies, providing technical support for the deep application of artificial intelligence in fields such as medicine, education, and entertainment.
These projects not only represent the platform’s technological innovations and research breakthroughs in multiple fields, but they also reflect the platform’s important role in the national science and technology strategy. Through the implementation of these research projects, the platform continuously drives the development of cutting-edge technologies, further enhancing its research strength and international influence.
Achievements
Since its establishment, the Health Intelligent Perception and Digital Twin Engineering Research Center of the Ministry of Education has made significant scientific achievements, relying on its solid research foundation and technological innovation capabilities. By undertaking major scientific projects and collaborating with top international research institutions and enterprises, the platform has made groundbreaking progress in various fields, making important contributions to global technological innovation. Below are the major scientific achievements of the platform:
The platform has published over 1,500 academic papers in prestigious international academic journals, with more than 1,100 SCI-indexed papers. Over 700 of these papers have been published in IEEE Transactions series journals, which highlights the platform's excellence in academic research. These papers cover a wide range of cutting-edge fields, including artificial intelligence, affective computing, brain science, and many others, significantly advancing the development of these disciplines. The platform has also received multiple best paper awards, and more than 80 of its papers have been listed as highly cited papers in the top 1% on Web of Science, further consolidating its leadership in the academic community.
Some representative papers include:
1. Shuzhen Li, Tong Zhang, Bianna Chen, C. L. Philip Chen, MIA-Net: Multi-Modal Interactive Attention Network for Multi-Modal Affective Analysis, in IEEE Transactions on Affective Computing, vol. 14, no. 4, pp. 2796-2809, 1 Oct.-Dec. 2023.
2. Xinrong Gong, C. L. Philip Chen, Bin Hu, Tong Zhang, CiABL: Completeness-induced Adaptive Broad Learning for Cross-Subject Emotion Recognition with EEG and Eye Movement Signals, in IEEE Transactions on Affective Computing, doi: 10.1109/TAFFC.2024.3392791.
3. Shuzhen Li, Tong Zhang, C. L. Philip Chen, SIA-Net: Sparse Interactive Attention Network for Multimodal Emotion Recognition, in IEEE Transactions on Computational Social Systems, doi: 10.1109/TCSS.2024.3409715.
4. Bingxiu Liu, Jifeng Guo, C. L. Philip Chen, Xia Wu, Tong Zhang, Fine-grained Interpretability for EEG Emotion Recognition: Concat-aided Grad-CAM and Systematic Brain Functional Network, in IEEE Transactions on Affective Computing, vol. 15, no. 2, pp. 671-684, April-June 2024, doi: 10.1109/TAFFC.2023.3288885.
5. Tong Zhang, Xuehan Wang, Xiangmin Xu, C. L. Philip Chen, GCB-Net: Graph Convolutional Broad Network and Its Application in Emotion Recognition, in IEEE Transactions on Affective Computing, vol. 13, no. 1, pp. 379-388, 1 Jan.-March 2022 (SCI).
6. Qilin Li, Tong Zhang, C. L. Philip Chen, Ke Yi, Long Chen, Residual GCB-Net: Residual Graph Convolutional Broad Network on Emotion Recognition, in IEEE Transactions on Cognitive and Developmental Systems, 2022, doi: 10.1109/TCDS.2022.3147839.
7. Hongbo Gao, Juping Zhu, Tong Zhang, Guotao Xie, Zhen Kan, Zhengyuan Hao, Kang Liu, Situational Assessment for Intelligent Vehicles Based on Stochastic Model and Gaussian Distributions in Typical Traffic Scenarios, in IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 52, no. 3, pp. 1426-1436, March 2022, DOI: 10.1109/TSMC.2020.3019512.
8. Xinrong Gong, Tong Zhang, C. L. Philip Chen, and Zhulin Liu, Research Review for Broad Learning System: Algorithms, Theory, and Applications, in IEEE Transactions on Cybernetics, vol. 52, no. 9, pp. 8922-8950, Sept. 2022.
9. Tong Zhang, Yuan Zong, Wenming Zheng, C. L. Philip Chen, Xiaopeng Hong, Chuangao Tang, Zhen Cui, Guoying Zhao, Cross-Database Micro-Expression Recognition: A Benchmark, in IEEE Transactions on Knowledge and Data Engineering, vol. 34, no. 2, pp. 544-559, 1 Feb. 2022 (SCI).
10. H. Huang, T. Zhang, C. Yang, and C. L. P. Chen, Motor Learning and Generalization Using Broad Learning Adaptive Neural Control, in IEEE Transactions on Industrial Electronics, vol. 67, no. 10, pp. 8608-8617, Oct. 2020.
11. Zongyan Zhang, C. L. Philip Chen, Haohan Weng, Tong Zhang, Self-Prompt Guided Image Outpainting Model for Captions Absence in Social Scenes, in IEEE Transactions on Computational Social Systems, doi: 10.1109/TCSS.2025.3547421.
12. Y. He, C. L. P. Chen, B. Chen, and T. Zhang, Enhancing Generalized EEG Classification with Decomposed Statistics-diverse Feature Augmentation, ICASSP 2025 - IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Hyderabad, India, 2025, pp. 1-5, doi: 10.1109/ICASSP49660.2025.10887610.
13. Zihua Xu, C. L. Philip Chen, Tong Zhang, TFAGL: A Novel Agent Graph Learning Method Using Time-Frequency EEG For Major Depressive Disorder Detection, in IEEE Transactions on Affective Computing, doi: 10.1109/TAFFC.2025.3527459.
14. Yikai Li, C. L. Philip Chen, Tong Zhang, Co-Training Broad Siamese-Like Network for Coupled-View Semi-Supervised Learning, in IEEE Transactions on Cybernetics, doi: 10.1109/TCYB.2025.3531441.
15. Jiaxin Guo, C. L. Philip Chen, Shuzhen Li, and Tong Zhang, Deuce: Dual-diversity Enhancement and Uncertainty-awareness for Cold-start Active Learning, Transactions of the Association for Computational Linguistics, 12:1736–1754, 2024.
The Health Intelligent Perception and Digital Twin Engineering Research Center places significant emphasis on technological innovation and intellectual property protection. To date, the platform has applied for over 200 patents, covering various fields such as artificial intelligence, affective computing, EEG signal processing, and intelligent wearable devices. The granting of these patents not only solidifies the platform’s leadership in technological innovation but also provides technological guarantees and application solutions for the development of related industries.
Some representative patents include:
1. Bian Na Chen, Tong Zhang, Jian Xiu Jin, C. L. Philip Chen, Multimodal Fusion Method, Device, Medium, and Equipment Based on Normalized Mutual Information, 202010156708.1, March 9, 2020, Authorized Date: March 21, 2023, Authorization No.: CN111461176B.
2. Yi Kai Li, Tong Zhang, C. L. Philip Chen, Edge Computing Method, Device, Medium, and Equipment for Stacked Broad Learning Systems, CN202110630388.3, June 7, 2021, Authorized Date: April 7, 2023, Authorization No.: CN113379779B.
3. C. L. Philip Chen, Meng Qing Ye, Tong Zhang, Brainwave Emotion Recognition Method, Medium, and Device Based on Dual-Space Adaptive Fusion, Application No.: CN202211161210.X, September 23, 2022, Authorized Date: April 7, 2023, Authorization No.: CN115238835B.
4. Xue Yan Kang, Jian Xiu Jin, Tong Zhang, C. L. Philip Chen, Zhu Lin Liu, Multicore Width Learning EEG Emotion Classification Method, Device, Medium, and Equipment, 202110290152X, March 18, 2021, Authorized Date: March 21, 2023, Authorization No.: CN113011493B.
5. Tong Zhang, Xue, Hu Bin, Xu Xiang Min, C. L. Philip Chen, Biological Feedback-Based Mental State Intervention and Regulation System and its Working Method, 201811431066.0, November 28, 2018, Invention Patent, Authorized Date: October 12, 2020, Authorization No.: CN109620257A.
6. Tong Zhang, Xue, Hu Bin, Xu Xiang Min, C. L. Philip Chen, Head-worn Device, 201821979383.1, November 28, 2018, Utility Model Patent, Authorized Date: January 14, 2020, Authorization No.: CN209932734U.
7. Xu Xiang Min, Huang Ye Lin, Tong Zhang, Portable Multi-Physiological Parameter Collection Device for Human Head, 201720449297.9, April 26, 2017, Utility Model Patent, Authorized Date: June 19, 2018, Authorization No.: CN207506574U.
8. Tong Zhang, Xue, Intelligent Brainwave Music Wearable Device for Mental State Regulation, 201911179114.6, November 27, 2019, Invention Patent, Authorized Date: July 16, 2021, Authorization No.: CN110947076B.
9. Tong Zhang, Xue Yan Kang, Gao Qing Chun, C. L. Philip Chen, Liao Li, Zeng Xian Fan, Li Xue Long, Zhou Jie, Huang Ru Xun, Li Xian Liang, Fu Xian, Liu Hong Ying, Zhou Zhen Wei, Fang Ji Qian, Huang Can, Hong Hua, System for Regulating Blood Pressure Based on Optimal Blood Pressure Target Value, 202010448175.4, May 25, 2020, Authorized Date: August 10, 2021, Authorization No.: CN111631700B.
10. Tong Zhang, Wu Meng Qi, Wang Jin Xuan, C. L. Philip Chen, Emotion Recognition Method, Medium, and Device Based on Head-worn Devices, 2024102237477, February 29, 2024, Authorized Date: June 21, 2024, Authorization No.: CN117809354.
11. Tong Zhang, Ye Han Yun, C. L. Philip Chen, Adaptive Attention Mechanism for Facial Expression Recognition Model Generation Method, Medium, and Equipment, CN202210298795.3, March 24, 2022, Authorized Date: August 6, 2024, Authorization No.: CN114863508.
12. Tong Zhang, Deng Zhong Yi, C. L. Philip Chen, Generation Matching Large Model Construction Method, Medium, and Equipment for Customer Service Scenarios, 2023117601974, December 20, 2023, Authorized Date: August 27, 2024, Authorization No.: CN117709969.
The granting of these patents has further consolidated the platform’s leadership in intelligent hardware, brain science, and emotional recognition fields, providing solid support for the commercialization and marketization of these technologies.
The Health Intelligent Perception and Digital Twin Engineering Research Center has repeatedly won prestigious awards both domestically and internationally throughout its years of scientific innovation. The platform’s research team has won the Wu Wenjun Artificial Intelligence Award four times, the IEEE Norbert Wiener Award (the highest honor in systems science) once, Guangdong Province’s Science and Technology Progress First Prize four times, and the Second Prize three times. It has also won the China Image and Graphics Society Science and Technology Progress Second Prize once. These awards not only represent high recognition for the platform team's outstanding work but also demonstrate the platform’s exceptional contributions to advancing computer science and technological innovation.
The platform’s scientific achievements span multiple cutting-edge fields, advancing the development of artificial intelligence, affective computing, intelligent hardware, and other technologies. These achievements have significantly promoted the transformation and application of scientific results, contributing to the socio-economic development and industry upgrading.
Platform Construction ：
In terms of site construction, the Engineering Center currently has approximately 4000 square meters of laboratory and office space, located within the School of Computer Science and Engineering at South China University of Technology's University Town campus, which can fully meet the current operational and research needs of the Center. To support future development, the Center plans to add 2000 square meters of space at the Guangzhou International Campus, specifically for conducting research on fundamental AI algorithms, key technology, and demonstration applications, thereby forming a cross-campus collaborative R&D layout. The Center will strive to optimize the allocation of site resources and implement dynamic management to improve space utilization efficiency.
At the funding guarantee level, the Center receives stable support from both South China University of Technology and social resources. It currently has research funds of 30 million RMB for various infrastructure construction and research activities. During the construction period, the university will continue its investment, while the Center will integrate social resources through various channels such as policy support and enterprise cooperation, with an expected additional investment of about 10 million RMB. These funds will strongly guarantee the Center's infrastructure construction, daily operations, scientific research, and talent introduction, providing solid support for the smooth progress of core tasks.
In terms of instrument and equipment configuration, the Center has made significant investments, with planned funding for equipment procurement not less than 20 million RMB. It is already equipped with a large number of advanced devices, constituting strong hardware support capabilities, mainly including distributed high-performance server clusters, various models of EEG acquisition equipment (such as wireless dry electrode EEG systems), intelligent psychological consultation equipment, and multimodal data collectors, providing reliable guarantees for conducting related frontier research. Particularly notable is the Center's access to up to 20P of AI computing resources provided by the Peng Cheng Laboratory Guangzhou Base. This computing power, based on Huawei's Ascend 910 series chips, possesses excellent data processing capabilities. All equipment is managed autonomously and scheduled centrally by the Center, ensuring efficient sharing and utilization of resources.
Cooperation and Communication：
In terms of research project cooperation, leveraging its own platform advantages, the Engineering Center actively undertakes and carries out multiple major technology R&D and industrialization projects. From 2023 to 2024, the Center's team newly undertook a total of 8 scientific research and development tasks, which effectively promoted the transformation of core technologies from research to practical application.
At the industry-academia-research cooperation level, the Center always adheres to the principles of open collaboration and mutual benefit, establishing long-term and in-depth cooperative relationships with technology enterprises and related units in the information field both domestically and internationally. By providing multiple technology R&D cooperation services to enterprises within the industry, the Center applies frontier research results to industrial practice, directly contributing to the technological progress and development of partner enterprises.
In terms of cooperation strategy and effectiveness, the Center upholds the concept of sustainable development, carrying out all cooperation and exchange activities based on the premise of taking the lead and aligning with its own medium and long-term development strategy. This pragmatic cooperation model, focusing on long-term benefits and common progress, has proven effective in practice for promoting the synergistic development and overall enhancement of all cooperating parties, forming a interactive ecosystem of industry-academia-research.
Talent development：
In terms of talent introduction and team building, the Engineering Center has formulated a systematic plan for the introduction and cultivation of high-level talents. This plan focuses on core areas such as intelligent perception technology, intelligent information transmission technology, and fundamental AI theory, aiming to select and appoint academic leaders with internationally leading standards and excellent academic backbone. Through this initiative, the Center has successfully built a stable-structured and innovative research team.
In the cultivation and training of young and middle-aged, the Center attaches great importance to the growth of young and middle-aged academic talents, actively creating development opportunities for them. By encouraging and supporting them to undertake national-level and provincial/ministerial major research projects such as National Science and Technology Innovation Major Special Projects and National Key R&D Programs, they are fully trained in high-level research practice, rapidly growing and domestic and international academic frontiers. Currently, the young and middle-aged researchers at the Center have undertaken important projects in multiple research directions such as intelligent perception, intelligent transmission, and brain-inspired intelligence, growing into backbone forces in academic and technological fields, and achieving significant results. For example, Professor Tong Zhang won the first prize of the Natural Science Award from the Guangdong Artificial Intelligence Industry Association in 2024.
In terms of reserve talent cultivation and team structure optimization, the Center continuously strengthens the cultivation of young and middle-aged professional technical talents and focuses on attracting and nurturing master's and doctoral students with rich engineering experience and solid foundations. This effort has created a high-level R&D echelon with a reasonable structure and stable personnel, ranging from senior experts to young graduate students. The Center provides an excellent R&D environment and solid technical support for talents at different levels, ensuring the sustainability of the talent cultivation system and reserving sufficient backup for the platform's long-term development.
Openness and Sharing：
The Engineering Center closely focuses on local needs, providing strong support for regional economic and industrial development through technology transfer and collaborative innovation. In serving the local economy, the Center's technological achievements have been widely applied in the Guangdong-Hong Kong-Macao Greater Bay Area and other domestic regions, directly creating significant economic benefits. For example, the developed multimodal sleep health intelligent assessment system, digital parallel human mental health interaction system, etc., have been put into use in hospitals and communities in Guangdong, Hunan, and other places.
In terms of technical support and services, the Engineering Center is committed to frontier technologies to the grassroots level, enhancing regional public service capabilities. The Center, in conjunction with top local medical institutions such as the First Affiliated Hospital of Guangzhou Medical University and the Second Xiangya Hospital, has established joint laboratories and demonstration centers, radiating clinically validated intelligent diagnosis and treatment systems to regional mental health centers and grassroots medical institutions. By deploying portable EEG acquisition equipment, cloud analysis platforms, and digital human terminals, the screening and diagnosis efficiency for psychological and mental diseases at grassroots units has been significantly improved, and service costs reduced.
Contact Information：
Center Director: C. L. Philip Chen
Contact Person: Tong ZhangEmail: tony@scut.edu.cnAddress: B3 Building, School of Computer Science and Engineering, South China University of Technology, 382 East Outer Ring Road, Panyu, Guangzhou, ChinaPostal Code: 510006
