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Toward Efficient and Privacy-Aware eHealth Systems: An Integrated Sensing, Computing, and Semantic Communication Approach

Yinchao Yang, Yahao Ding, Zhaohui Yang, Chongwen Huang, Zhaoyang Zhang, Dusit Niyato, Mohammad Shikh-Bahaei

TL;DR

This paper introduces Integrated Sensing, Computing, and Semantic Communication (ISCSC) for privacy-aware eHealth by fusing radar-based vital-sign sensing with semantically compressed communications. It leverages an IMM filter for robust patient motion tracking and predictive beamforming, and jointly optimizes beamforming matrices and semantic extraction ratios to maximize semantic secrecy rate while preserving sensing accuracy under computing and power constraints. Through a detailed performance framework including semantic rate, SSR, CRB-based sensing metrics, and computational power, the study demonstrates that ISCSC can outperform conventional joint sensing and communication approaches in both data efficiency and privacy preservation. The results indicate practical potential for real-time, privacy-conscious remote health monitoring, and point to future work in hardware prototyping, learning-enabled robustness, and multi-robot collaboration.

Abstract

Real-time and contactless monitoring of vital signs, such as respiration and heartbeat, alongside reliable communication, is essential for modern healthcare systems, especially in remote and privacy-sensitive environments. Traditional wireless communication and sensing networks fall short in meeting all the stringent demands of eHealth, including accurate sensing, high data efficiency, and privacy preservation. To overcome the challenges, we propose a novel integrated sensing, computing, and semantic communication (ISCSC) framework. In the proposed system, a service robot utilises radar to detect patient positions and monitor their vital signs, while sending updates to the medical devices. Instead of transmitting raw physiological information, the robot computes and communicates semantically extracted health features to medical devices. This semantic processing improves data throughput and preserves the clinical relevance of the messages, while enhancing data privacy by avoiding the transmission of sensitive data. Leveraging the estimated patient locations, the robot employs an interacting multiple model (IMM) filter to actively track patient motion, thereby enabling robust beam steering for continuous and reliable monitoring. We then propose a joint optimisation of the beamforming matrices and the semantic extraction ratio, subject to computing capability and power budget constraints, with the objective of maximising both the semantic secrecy rate and sensing accuracy. Simulation results validate that the ISCSC framework achieves superior sensing accuracy, improved semantic transmission efficiency, and enhanced privacy preservation compared to conventional joint sensing and communication methods.

Toward Efficient and Privacy-Aware eHealth Systems: An Integrated Sensing, Computing, and Semantic Communication Approach

TL;DR

This paper introduces Integrated Sensing, Computing, and Semantic Communication (ISCSC) for privacy-aware eHealth by fusing radar-based vital-sign sensing with semantically compressed communications. It leverages an IMM filter for robust patient motion tracking and predictive beamforming, and jointly optimizes beamforming matrices and semantic extraction ratios to maximize semantic secrecy rate while preserving sensing accuracy under computing and power constraints. Through a detailed performance framework including semantic rate, SSR, CRB-based sensing metrics, and computational power, the study demonstrates that ISCSC can outperform conventional joint sensing and communication approaches in both data efficiency and privacy preservation. The results indicate practical potential for real-time, privacy-conscious remote health monitoring, and point to future work in hardware prototyping, learning-enabled robustness, and multi-robot collaboration.

Abstract

Real-time and contactless monitoring of vital signs, such as respiration and heartbeat, alongside reliable communication, is essential for modern healthcare systems, especially in remote and privacy-sensitive environments. Traditional wireless communication and sensing networks fall short in meeting all the stringent demands of eHealth, including accurate sensing, high data efficiency, and privacy preservation. To overcome the challenges, we propose a novel integrated sensing, computing, and semantic communication (ISCSC) framework. In the proposed system, a service robot utilises radar to detect patient positions and monitor their vital signs, while sending updates to the medical devices. Instead of transmitting raw physiological information, the robot computes and communicates semantically extracted health features to medical devices. This semantic processing improves data throughput and preserves the clinical relevance of the messages, while enhancing data privacy by avoiding the transmission of sensitive data. Leveraging the estimated patient locations, the robot employs an interacting multiple model (IMM) filter to actively track patient motion, thereby enabling robust beam steering for continuous and reliable monitoring. We then propose a joint optimisation of the beamforming matrices and the semantic extraction ratio, subject to computing capability and power budget constraints, with the objective of maximising both the semantic secrecy rate and sensing accuracy. Simulation results validate that the ISCSC framework achieves superior sensing accuracy, improved semantic transmission efficiency, and enhanced privacy preservation compared to conventional joint sensing and communication methods.

Paper Structure

This paper contains 29 sections, 1 theorem, 56 equations, 13 figures, 3 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. System Model
  3. Signal Model
  4. Communication Model
  5. Sensing Model
  6. Estimation of Patient Position and Physiological Parameters
  7. Patient Motion Tracking via IMM Filter
  8. State Evolution Model
  9. IMM Filtering for Patient Motion Tracking
  10. Model Interaction
  11. Elementary Filtering
  12. Model Probability Update
  13. Performance Indicators
  14. Semantic Communication
  15. Computing and Power Consumption
  16. ...and 14 more sections

Key Result

Proposition 1

The optimal value of $\rho_k$, denoted by $\rho_k^*$, is equal to $\min \left( \max \left( \frac{\iota D_k}{\eta^* F}, \rho_{LB, k} \right) , \rho_{UB, k} \right)$, where $\eta$ is the Lagrange multiplier and $\eta^*$ is the optimal value.

Figures (13)

  • Figure 1: An illustration of the performance Pareto boundary of ISAC and ISCSC systems.
  • Figure 2: An ISCSC-enabled eHealth system, where a service robot engages in semantic communication with multiple medical devices and performs sensing of multiple patients to determine their locations and vital signs.
  • Figure 3: Flowchart of the proposed ISCSC eHealth system, illustrating the key components.
  • Figure 4: An illustration of the approximation of zhao2025compression using \ref{['eq18']}. The parameters are given as $[C_1, C_2, F]$.
  • Figure 5: Normalised SSR versus normalised RCRB for different values of $\rho_k$ and $F$.
  • ...and 8 more figures

Theorems & Definitions (6)

  • Remark 1
  • Remark 2
  • Remark 3
  • proof
  • Proposition 1
  • proof