PPG Sensor-Based Biometric Identification and Physiological Analysis via Temporal-Frequency Disentanglement With Liquid Neural Networks

Photoplethysmography (PPG) sensors support both physiological monito- ring and biometric identification, making them key components in wearable sensing systems. However, real-world applications face challenges from signal nonstationarity and physiological variability. This work proposes a temporal-frequency manifold disentanglement framework to improve the robustness and accuracy of PPG-based biometric recognition. A closed-form continuous-time (CfC) liquid neural network captures temporal and spectral features from raw PPG signals, while an orthogonal manifold projection separates identity-related and physiological representations. To support physiological analysis, we construct and release a new multiphysiological PPG dataset with synchronized annotations for body mass index (BMI), blood pressure, blood glucose, and heart rate. Our method achieves 94.12% accuracy (F1-score: 0.93), outperforming eight state-of-the-art approaches. Further analysis reveals that BMI, blood glucose, and heart rate strongly influence identity features, highlighting the need for physiologically aware modeling in sensor systems. The proposed framework enhances PPG sensor signal interpretation, offering a scalable solution for real-time biometric sensing applications.