Transforming Healthcare: Intelligent Wearable Sensors Empowered by Smart Materials and Artificial Intelligence.

Journal: Advanced materials (Deerfield Beach, Fla.)
Published Date:

Abstract

Intelligent wearable sensors, empowered by machine learning and innovative smart materials, enable rapid, accurate disease diagnosis, personalized therapy, and continuous health monitoring without disrupting daily life. This integration facilitates a shift from traditional, hospital-centered healthcare to a more decentralized, patient-centric model, where wearable sensors can collect real-time physiological data, provide deep analysis of these data streams, and generate actionable insights for point-of-care precise diagnostics and personalized therapy. Despite rapid advancements in smart materials, machine learning, and wearable sensing technologies, there is a lack of comprehensive reviews that systematically examine the intersection of these fields. This review addresses this gap, providing a critical analysis of wearable sensing technologies empowered by smart advanced materials and artificial Intelligence. The state-of-the-art smart materials-including self-healing, metamaterials, and responsive materials-that enhance sensor functionality are first examined. Advanced machine learning methodologies integrated into wearable devices are discussed, and their role in biomedical applications is highlighted. The combined impact of wearable sensors, empowered by smart materials and machine learning, and their applications in intelligent diagnostics and therapeutics are also examined. Finally, existing challenges, including technical and compliance issues, information security concerns, and regulatory considerations are addressed, and future directions for advancing intelligent healthcare are proposed.

Authors

  • Shuwen Chen
    School of Physics and Information Engineering, Jiangsu Second Normal University, Nanjing, 211200, China.
  • Shicheng Fan
    Department of Biomedical Engineering, National University of Singapore, Singapore, 117583, Singapore.
  • Zheng Qiao
    Department of Biomedical Engineering, National University of Singapore, Singapore, 117583, Singapore.
  • Zixiong Wu
    Department of Biomedical Engineering, National University of Singapore, Singapore, 117583, Singapore.
  • Baobao Lin
    Department of Biomedical Engineering, National University of Singapore, Singapore, 117583, Singapore.
  • Zhijie Li
    Department of Design, Silla University, Busan 46958, Republic of Korea.
  • Michael A Riegler
    SimulaMet, Oslo, Norway.
  • Matthew Yu Heng Wong
    School of Clinical Medicine, University of Cambridge, Cambridge, CB2 1TN, UK.
  • Arve Opheim
    Sunnaas Rehabilitation Hospital, Bjoernemyr, 1453, Norway.
  • Olga Korostynska
    Department of Mechanical, Electronic and Chemical Engineering (MEK), Faculty of Technology, Art, and Design, TKD, Oslo Metropolitan University, OsloMet, Oslo, 0166, Norway.
  • Kaare Magne Nielsen
    Department of Life Science and Health, Faculty of Health Sciences, Oslo Metropolitan University, OsloMet, Oslo, 0130, Norway.
  • Thomas Glott
  • Anne Catrine T Martinsen
    Sunnaas Rehabilitation Hospital, Bjoernemyr, 1453, Norway.
  • Vibeke H Telle-Hansen
    Intelligent Health, Faculty of Health Sciences and Faculty of Technology, Art and Design, Oslo Metropolitan University, OsloMet, Oslo, 0130, Norway.
  • Chwee Teck Lim
    NUS Graduate School for Integrative Sciences & Engineering, National University of Singapore, Singapore, Singapore. ctlim@nus.edu.sg.