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.

翻译：一维生物信号的数字孪生能够实现对个体生理过程的实时监测，从而助力早期疾病诊断与个性化治疗。本研究在6G/WiFi通感一体化系统框架下，提出了一种创新的非接触式光电容积脉搏波数字孪生信号合成方法。我们采用工作频率为5.23 GHz的软件定义无线电设备，向邻近人员的胸腔发射宽带6G/WiFi信号并采集反射信号，由此构建了包含30名健康受试者、总时长300分钟的Radio-PPG数据集。该数据集包含近同步采集的64通道射频数据、PPG数据及三类生命体征标签。基于此，我们测试了两种用于数字孪生PPG信号合成的人工智能模型：i）离散余弦变换结合多层感知机，ii）级联双U-NET网络（近似网络与精修网络）配合定制损失函数。实验结果表明，U-NET模型在数字孪生PPG合成中取得了0.194的相对平均绝对误差，且仅需15.62%的ISAC感知开销。进一步通过对合成数字孪生PPG进行质量评估，包括基于数字孪生PPG的生命体征估计精度与特征提取能力测试，发现其性能与参考PPG的相应评估结果相当。本工作彰显了生成式人工智能与6G/WiFi通感一体化技术的融合潜力，为开发面向新冠肺炎、心血管疾病筛查及特殊人群健康状态评估的非接触式检测工具奠定了重要基础。