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

2025-10-13

AI Summary

To address the dual challenges of non-contact vital sign monitoring and privacy-preserving communication in telemedicine, this paper proposes a novel eHealth framework integrating millimeter-wave (mmWave) radar sensing, edge-based semantic feature extraction, and semantic communication. It pioneers the application of semantic communication to privacy-sensitive healthcare scenarios: by compressing physiological signals into semantic features (e.g., respiration and heart rate metrics only), applying semantic-level encryption, and jointly optimizing estimation accuracy via an Interacting Multiple Model (IMM) filter at the edge, the framework achieves high-fidelity physiological parameter estimation with minimal information leakage. Furthermore, it synergistically optimizes sensing accuracy, communication efficiency, and privacy protection under computational and energy constraints by combining location-aware beamforming with a semantic secrecy rate maximization algorithm. Simulation results demonstrate significant improvements over conventional approaches in sensing accuracy, semantic transmission efficiency, and privacy assurance strength.

Technology Category

• Machine Learning: Privacy
• Application Domains: Internet of Things, Sensor Networks & Smart Cities
• Computer Vision: Multi-modal Vision

Application Category

• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments

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.

Problem

• Enhancing eHealth efficiency with integrated sensing and semantic communication
• Preserving patient privacy through semantic feature extraction
• Optimizing beamforming and semantic ratio for secure monitoring

Innovation

• Radar-based contactless vital signs monitoring
• Semantic communication of extracted health features
• Joint optimization of beamforming and semantic extraction