A Deep Spatial–Temporal Graph Modeling and IoMT-Enabled Framework for Driver Autonomic Nervous System Condition Prediction via Dual Optical Fiber Sensor

Automatic assessment of driver autonomic nervous system conditions is crucial for enhancing driving safety and healthcare. We present a novel approach that combines a dual optical fiber sensor system and a sophisticated deep learning framework, VHDP, with a strong emphasis on graph learning and spatiotemporal modeling techniques. The proposed fiber interferometer based dual optical fiber sensor system can effectively monitor driver vital signs in various environments. The VHDP framework, a significant innovation in deep learning, first utilizes the EMGLCN module to extract spatial and temporal features from the acquired heart rate variability (HRV) data for graph modeling. Then, through the dynamic spatial–temporal multigraph method and the temporal-awareness attention (TAA) module, it captures cross-time and dimensional correlations. Finally, the prior knowledge guided recalibration fusion (PKGRF) module generates accurate outputs. Experimental results show that the mean square error (MSE), mean absolute error (MAE) and R-square (R2) reach 2.354, 0.896, and 0.9857, respectively, outperforming state-of-the-art approaches. This work not only provides a new method for long-term driver HRV assessment and forecasting but also demonstrates the potential of graph learning and spatiotemporal modeling in the fields of medical monitoring and artificial intelligence, offering valuable insights for the development of portable vital signs monitoring devices in the context of the Internet of Medical Things (IoMT).