Radio-PPG: photoplethysmogram digital twin synthesis using deep neural representation of 6G/WiFi ISAC signals
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866918148872077312 |
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| author | Filho, Israel Jesus Santos Rahman, Muhammad Mahboob Ur Laleg-Kirati, Taous-Meriem Al-Naffouri, Tareq |
| author_facet | Filho, Israel Jesus Santos Rahman, Muhammad Mahboob Ur Laleg-Kirati, Taous-Meriem Al-Naffouri, Tareq |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_22326 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Radio-PPG: photoplethysmogram digital twin synthesis using deep neural representation of 6G/WiFi ISAC signals Filho, Israel Jesus Santos Rahman, Muhammad Mahboob Ur Laleg-Kirati, Taous-Meriem Al-Naffouri, Tareq Information Theory Signal Processing 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. |
| title | Radio-PPG: photoplethysmogram digital twin synthesis using deep neural representation of 6G/WiFi ISAC signals |
| topic | Information Theory Signal Processing |
| url | https://arxiv.org/abs/2509.22326 |