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Next-generation digital twin model with unobtrusive RF multi-sensing for AI-based human monitoring
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  • Published: 01 April 2026

Next-generation digital twin model with unobtrusive RF multi-sensing for AI-based human monitoring

  • Sagheer Khan1,
  • Aaesha Alzaabi2,3 na1,
  • Imran M. Saied2,3 na1,
  • Usman Anwar2,3 na1,
  • Kiran Khurshid4 na1 &
  • …
  • Tughrul Arslan2,3 na1 

Scientific Reports , Article number:  (2026) Cite this article

We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Subjects

  • Computational biology and bioinformatics
  • Engineering
  • Health care
  • Mathematics and computing

Abstract

In healthcare, Digital Twin (DT) model can create real-time digital representations of patients’ physiological states, enabling intelligent, data-driven decision-making for continuous monitoring and personalized treatment-without the invasiveness of traditional methods. This study presents a next-generation RF-powered DT model, integrating AI-driven analytics with multi-vital human sensing to establish a virtualized, self-learning, and adaptive framework for health monitoring. Leveraging unobtrusive Radio Frequency (RF) sensors, advanced signal processing, and Artificial Intelligence (AI), the proposed DT model facilitates continuous, non-contact assessment of respiration and exhaled hydration, ensuring a scalable, cost-effective, and efficient approach to real-time physiological monitoring. This work integrates unobtrusive ESP32 Wi-Fi sensors with Channel State Information (CSI) for respiration tracking and flexible UWB RF sensors for hydration monitoring. Enhanced signal processing achieves 100% accurate estimation of Breaths Per Minute (BPM) within the accepted inter-observer variability threshold (±5 BPM) from raw respiration data. To overcome the challenge of limited real-world respiration and hydration datasets, novel statistical augmentation methods are employed to generate synthetic data, validated through cross-correlation techniques (Pearson, Kendall, and Spearman). AI models, including supervised and semi-supervised Machine Learning (ML) and Deep Learning (DL), are implemented for binary and multi-class classification of respiration and hydration data. Random Forest achieves top accuracies of 88% (binary) and 69% (multi-class) in semi-supervised classification, while Decision Tree attains 89% and 83% accuracy in supervised exhaled hydration classification. Additionally, K-Nearest Neighbors (KNN) achieves 93% (binary) and 89% (multi-class) accuracy in semi-supervised hydration classification.

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Data availability

The data is available at IEEE Dataport: https://dx.doi.org/10.21227/rzc7-ma61

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Funding

This research was funded by the Legal & General Group (research grant to establish the independent Advanced Care Research Centre at University of Edinburgh). The funder had no role in conduct of the study, interpretation or the decision to submit for publication. The views expressed are those of the authors and not necessarily those of Legal & General.

Author information

Author notes

  1. Aaesha Alzaabi, Imran M. Saied, Usman Anwar, Kiran Khurshid and Tughrul Arslan have contributed equally to this work.

Authors and Affiliations

  1. SDAIA-KFUPM Joint Research Center for Artificial Intelligence, KFUPM, Dhahran, Saudi Arabia

    Sagheer Khan

  2. Institute for Integrated Micro and Nano Systems, The University of Edinburgh, Edinburgh, EH9 3FF, Scotland, UK

    Aaesha Alzaabi, Imran M. Saied, Usman Anwar & Tughrul Arslan

  3. Advanced Care Research Centre (ACRC), The University of Edinburgh, Edinburgh, EH16 4UX, UK

    Aaesha Alzaabi, Imran M. Saied, Usman Anwar & Tughrul Arslan

  4. Department of Computer and Software Engineering, National University of Sciences and Technology (NUST), Islamabad, 44000, Pakistan

    Kiran Khurshid

Authors
  1. Sagheer Khan
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  2. Aaesha Alzaabi
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  3. Imran M. Saied
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  4. Usman Anwar
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  5. Kiran Khurshid
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  6. Tughrul Arslan
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Contributions

Conceptualization:[Sagheer khan, Tughrul Arslan], Methodology:[Sagheer khan, Tughrul Arslan], Software:[Sagheer Khan,Aaesha Alzaabi,Imran M. Saied], Investigation: [Sagheer Khan, Usman Anwar, Aaesha Alzaabi], Formal analysis:[Sagheer Khan, Kiran Khurshid, Tughrul Arslan], Data Collection: [Aaesha Alzaabi, Imran M. Saied, Data Collection], Writing—original and Editing:[Sagheer Khan], Validation:[Sagheer Khan, Usman Anwar, Kiran Khurshid], Proof-Reading:[Usman Anwar, Kiran Khurshid,Tughrul Arslan];

Corresponding author

Correspondence to Sagheer Khan.

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Cite this article

Khan, S., Alzaabi, A., Saied, I.M. et al. Next-generation digital twin model with unobtrusive RF multi-sensing for AI-based human monitoring. Sci Rep (2026). https://doi.org/10.1038/s41598-026-43984-w

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  • Received: 27 November 2025

  • Accepted: 09 March 2026

  • Published: 01 April 2026

  • DOI: https://doi.org/10.1038/s41598-026-43984-w

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Keywords

  • Artificial intelligence (AI)
  • Digital twin (DT)
  • Hydration monitoring
  • RF sensing
  • Respiration monitoring
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