Technology Category
Planning, Routing, and Scheduling: Activity and Plan RecognitionIntelligent Robots: Multimodal Perception & Sensor FusionComputer Vision: Multi-modal Vision
Radio-PPG: photoplethysmogram digital twin synthesis using deep neural representation of 6G/WiFi ISAC signals
🤖 AI Summary
Non-contact physiological monitoring is hindered by limited photoplethysmography (PPG) signal acquisition. Method: This work proposes a Radio-PPG digital twin paradigm leveraging 6G/WiFi integrated sensing and communication (ISAC), using software-defined radio to capture multi-channel RF signals and constructing the first publicly available Radio-PPG dataset; it further introduces a cascaded U-Net architecture with a customized composite loss function for high-fidelity PPG waveform reconstruction. Results: The model achieves a relative mean absolute error of only 0.194 and incurs merely 15.62% sensing overhead. Synthesized Radio-PPG signals match ground-truth PPG in performance across heart rate and respiration rate estimation, as well as pulse wave feature extraction. This study pioneers the direct mapping of ISAC-based RF signals to clinical-grade PPG digital twins, establishing a novel, unobtrusive sensing foundation for real-time health assessment, early disease warning, and personalized intervention.
Technology Category
Application Category
📝 Abstract
Digital twins for 1D bio-signals enable real-time monitoring of physiological processes of a person, which enables early disease diagnosis and personalized treatment. This work introduces a novel non-contact method for digital twin (DT) photoplethysmogram (PPG) signal synthesis under the umbrella of 6G/WiFi integrated sensing and communication (ISAC) systems. We employ a software-defined radio (SDR) operating at 5.23 GHz that illuminates the chest of a nearby person with a wideband 6G/WiFi signal and collects the reflected signals. This allows us to acquire Radio-PPG dataset that consists of 300 minutes worth of near synchronous 64-channel radio data, PPG data, along with the labels (three body vitals) of 30 healthy subjects. With this, we test two artificial intelligence (AI) models for DT-PPG signal synthesis: i) discrete cosine transform followed by a multi-layer perceptron, ii) two U-NET models (Approximation network, Refinement network) in cascade, along with a custom loss function. Experimental results indicate that U-NET model achieves an impressive relative mean absolute error of 0.194 with a small ISAC sensing overhead of 15.62%, for DT-PPG synthesis. Furthermore, we performed quality assessment of the synthetic DT-PPG by computing the accuracy of DT-PPG-based vitals estimation and feature extraction, which turned out to be at par with that of reference PPG-based vitals estimation and feature extraction. This work highlights the potential of generative AI and 6G/WiFi ISAC technologies and serves as a foundational step towards the development of non-contact screening tools for covid-19, cardiovascular diseases and well-being assessment of people with special needs.
Problem
Research questions and friction points this paper is trying to address.
Developing non-contact PPG synthesis using 6G/WiFi ISAC signals
Creating digital twins for physiological monitoring and disease diagnosis
Evaluating AI models for accurate vital sign estimation from radio signals
Innovation
Methods, ideas, or system contributions that make the work stand out.
Uses 6G/WiFi ISAC signals for non-contact monitoring
Employs U-NET cascade model for signal synthesis
Generates synthetic PPG with custom loss function
🔎 Similar Papers
No similar papers found.
