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Integration of multiple technologies amid Industry 5.0 is observed in most of the engineering application. However, Machine Learning (ML) and Digital Twin Technology (DTT) play a major role in data acquisition, processing, data analytics and decision making process of almost every automated system. Due to the gigantic amount of data generated in healthcare and medicine applications, manual analysis of this data becomes very cumbersome and time consuming. Therefore, ML, artificial intelligence (AI), data science, DTT and Cyber Physical Systems (CPSs) emerge as powerful tools for efficient, accurate, automated and fast processing of this medical data including 1D, 2D signals as well as multidimensional images. This paper presents an overview and comparative analysis of machine learning algorithms and tools utilized for brain monitoring applications especially focusing on microwave techniques. A systematic review and meta analysis based on PRISMA approach is presented and analyzed. A brief outline of ML algorithms utilized for different applications focusing on brain temperature measurement, stroke detection, and Intracranial Pressure (ICP) measurement are elaborated along with ML tools for microwave imaging.

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Next-Gen Microwave Sensing for Brain Monitoring: Fusion of Machine Learning and Digital Twin Technology

  • 会议论文

  • 发表日期：

    2026年

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  • Daljeet Singh,
  • Sarthak Acharya,
  • Rajkujmar Saini,
  • Hem Dutt Joshi,
  • Mariella Särestöniemi,
  • Teemu Myllylä

摘要

Integration of multiple technologies amid Industry 5.0 is observed in most of the engineering application. However, Machine Learning (ML) and Digital Twin Technology (DTT) play a major role in data acquisition, processing, data analytics and decision making process of almost every automated system. Due to the gigantic amount of data generated in healthcare and medicine applications, manual analysis of this data becomes very cumbersome and time consuming. Therefore, ML, artificial intelligence (AI), data science, DTT and Cyber Physical Systems (CPSs) emerge as powerful tools for efficient, accurate, automated and fast processing of this medical data including 1D, 2D signals as well as multidimensional images. This paper presents an overview and comparative analysis of machine learning algorithms and tools utilized for brain monitoring applications especially focusing on microwave techniques. A systematic review and meta analysis based on PRISMA approach is presented and analyzed. A brief outline of ML algorithms utilized for different applications focusing on brain temperature measurement, stroke detection, and Intracranial Pressure (ICP) measurement are elaborated along with ML tools for microwave imaging.