The book "Advanced wearable sensing in clinical practice towards healthcare 5.0" provides an overview of the state of the art that caters various professionals from clinical, scientific, and engineering backgrounds, as well as the novices that are interested in wearable sensing technology.
It presents a comprehensive overview of modern wearable technology and unmet challenges. The book covers research hotspots in wearable technology, including advanced materials, low-cost wearable sensing systems, bio-signal processing, multimodal data analysis, artificial intelligence, and internet-of-medical-things. The readers from various research backgrounds (e.g., biomedical engineering, artificial intelligence, material sciences, etc.) can get an in-depth understanding of current trends and future directions. It summarizes different technologies regarding the clinical needs, theoretical basis, latest technical innovations, challenges in implementation, and limitations. These details will help improve existing designs and algorithms to cater different application scenarios. The book also provides the details of the implementation of advanced wearable sensors in different clinical settings including population screening, clinical diagnosis, to remote healthcare monitoring. The targeted diseases include cardiovascular, neurological, respiratory, metabolic, and other chronic diseases. By analyzing relevant standards and regulations, the challenges for next-generation wearable sensors are discussed in the context of healthcare ecosystem from a perspective of healthcare 5.0.
In summary, the book focus on technical innovations but also delves into clinical and regulatory aspects. It caters a broad spectrum of readers. Clinical researchers, healthcare professionals, and students can obtain a better grasp of current trends of wearable technology. Biomedical engineers can understand the clinical needs, the state of the art, and the challenges in implementation, to develop better wearable sensors and algorithms for signal processing and data analysis. Public health researchers and policymakers can update the regulations and clinical guidelines to enable the integration of advanced wearable sensing technologies into current healthcare ecosystems.
Haipeng Liu received the bachelor’s and master’s degrees in engineering from Zhejiang University, China, in 2012 and 2015, respectively, and the Doctor of Philosophy degree in medical sciences from The Chinese University of Hong Kong, in 2018. From 2019 to 2020, he was a Research Fellow with the Medical Technology Research Centre, Anglia Ruskin University. From 2020 to 2024, he was a Research Fellow with Coventry University. He is an Assistant Professor, a Lecturer of three undergraduate and master course modules, and a Module Leader of one, and have been invited as a guest lecturer of European Institute of Innovation and Technology (EIT) Summer School, University Hospitals Coventry and Warwickshire NHS Trust, Nottingham Trent University (U.K.) and Rushford Business School (Switzerland). He is a PI and a mentor of undergraduate student internship project of Anglia Ruskin University, and a mentor of the National Medical Research Association (NMRA, U.K.). He is the author of over 120 journal articles, 30 book chapters, and 12 conference papers, with over 2800 citations and the H-index of 28. His research interests include computational simulation of cardio-cerebrovascular diseases, AI-enhanced diagnostics, and wearable sensor development. He is a recipient of the Travel Award of British Heart Foundation, the Research and Innovation Support Fund of Anglia Ruskin University, Official Development Assistance (ODA) funding and four Ph.D. grants from Coventry University. Dr Liu is an Associate Fellow of the Higher Education Academy (AFHEA, U.K.). He is a member of the World Stroke Organization (WSO), Chinese Stroke Association (CSA, China), British Society for Cardiovascular Research (BSCR, U.K.), and Cardiovascular Analytics Group (CVAG, HKSAR of China). He is an editorial member of nine academic journals. He is a peer-reviewer of more than 200 academic papers from over 50 academic journals, four international conferences, and two book proposals.
Gary Tse matriculated at Trinity Hall, University of Cambridge in 2005 to read pre-clinical medicine. He subsequently completed his clinical training at Imperial College London, as well as PhD and MD from the University of Cambridge. He is Associate Dean (Innovations and Research) at the School of Nursing and Health Sciences, Hong Kong Metropolitan University (HKMU). He is also Distinguished Chair Professor at the Department of Cardiology, Second Hospital of Tianjin Medical University, China and a Visiting Professor at the Faculty of Health and Medical Sciences, University of Surrey. Prior to joining HKMU, Prof. Tse held a joint position as Clinical Reader at the University of Kent (with permanent appointment until retirement age) and Public Health Consultant at the Medway Council in local government, UK. He serves as a Nucleus Committee Member of the Population Health Section, European Association of Preventive Cardiology. He is a Council Member, Secretary and President-Elect of the International Society of Electrocardiology, as well as a Member of the Board of Trustees of the International Society for Holter and Non-invasive Electrocardiology. He is a Fellow of the Faculty of Public Health, Royal College of Pathologists and Royal College of Physicians, and an elected Member of the European Academy of Sciences and Arts (Class II: Medicine). He has been listed on the World’s Top 2% Scientists Released by Stanford University for the Cardiovascular System & Hematology subfield since 2020. He is ranked 26 th on the Top Cardiovascular Researchers from China in 2023. He was placed Top 0.05% scientists globally, ranking 87 th globally for cardiology in 2024 by ScholarGPS. He ranked 3rd for cardiology in China in 2025 by AD Scientific Index. He has an H-index of 66 and has obtained more than HK$200 million research-related funding. He leads the Hong Kong Risk Modelling Team focusing on the use of big data and artificial intelligence for the development of predictive models for estimating biological age and risk prediction of cardiovascular and cerebrovascular diseases.
Pronaya Bhattacharya received the Ph.D. degree in optical networks from Dr. A. P. J. Abdul Kalam Technical University, Lucknow, Uttar Pradesh, India, in 2021. He is currently employed as an Assistant Professor with the Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad, India. He has over eight years of teaching experience. He has authored or coauthored more than 120 research papers in leading SCI journals and top core IEEE COMSOC A* conferences. Some of his top-notch findings are published in reputed SCI journals, like IEEE Journal of Biomedical and Health Informatics, IEEE Transactions on Vehicular Technology, IEEE Internet of Things Journal, IEEE Transactions on Network Science and Engineering, IEEE Access, ETT (Wiley), Expert Systems (Wiley), FGCS (Elsevier), OQEL (Springer), WPC (Springer), ACM-MOBICOM, IEEE-INFOCOM, IEEE-ICC, IEEECITS, IEEE-ICIEM, IEEE-CCCI, and IEEE-ECAI. His google scholar has 2126 citations to his credit with an H-index of 23 and i10-index of 53, and 1263 citations in Scopus, an H-index of 18. His research interests include healthcare analytics, optical switching and networking, federated learning, blockchain, and the IoT. He has been appointed as a Technical Committee Member and the Session Chair across the globe. He is a Lifetime Member of professional societies, like ISTE and IAENG. He was awarded the seven best paper awards in Springer ICRIC-2019, IEEE-ICIEM-2021, IEEE-ECAI-2021, and Springer COMS2-2021. He is a reviewer of 24 reputed SCI journals, like IEEE Internet of Things Journal, IEEE Transactions on Industrial Informatics, IEEE Access, IEEE Network, ETT (Wiley), IJCS (Wiley), MTAP (Springer), OSN (Elsevier), WPC (Springer), and others.
Xinhong Wang received the Bachelor of Medical Imaging Technology from Sichuan University in 2011, and Ph.D. in Biomedical Engineering from Zhejiang University. He has been engaged in mixed research in the fields of biomedical engineering and medicine. He is currently an associate professor at the The Second Affiliated Hospital, Zhejiang University School of Medicine. His research interests are focused on medical imaging technology, imaging equipment development, and computational hemodynamics.
Rajesh Kumar Tripathy received a Ph.D. in Machine Learning for Signal Processing from IIT Guwahati in 2017. He has also received B.Tech and M.Tech degrees in Electronics & Telecommunication and Biomedical Engineering from BPUT, Odisha, and NIT Rourkela, India. He is currently working as an associate professor at BITS Pilani Hyderabad, India, since 2024. Prior to this, he served for more than ten years as an assistant professor in reputed institutions. He has published 89 papers in reputed international journals. He has also published 13 conference papers, 3 edited books, and 9 chapters. He has supervised 3 Ph.D. students in machine learning and biomedical signal processing. He has also supervised 5 M.Tech projects and 25 B.Tech projects. He is extensively working in research areas such as biomedical signal processing, machine learning, deep learning for healthcare, time–frequency analysis, medical image processing, Internet of Things (IoT), and embedded systems for healthcare applications. He received the Outstanding Potential for Excellence in Research Award (OPERA) from BITS Pilani in 2018. He has received INR 2,280,000 in funding from BITS Pilani through an OPERA grant to conduct high-quality research on signal processing and machine learning for healthcare data analysis. He has completed one sponsored project as a co-principal investigator from the CARS project, DRDO, India. His research papers are cited more than 4369 times on Google Scholar (accessed on 05/11/2025). He is also working as the associate editor for reputed journals like IEEE Access and Frontiers in Physiology.
Goh Choon Hian is an Associate Professor and Head of the Department of Mechatronics and Biomedical Engineering at Universiti Tunku Abdul Rahman (UTAR), Malaysia. A registered Professional Engineer with the Board of Engineers Malaysia, he holds a Ph.D. and Bachelor’s degree in Biomedical Engineering from Universiti Malaya. His research spans biomedical engineering, biosensors, robotics, and healthcare-focused applications of machine learning and artificial intelligence. Dr. Goh’s interdisciplinary work bridges engineering and clinical medicine, with particular focus on falls, syncope, and patient monitoring technologies. He is committed to advancing biomedical innovation through collaborative, real-world solutions.
