Non‐Contact Photoplethysmography for In‐Vehicle Health Monitoring With Seamlessly Integrated Photodetector Array and Hardware‐Accelerated AI

ABSTRACT While non‐contact physiological monitoring offers clear advantages over contact‐based methods in dynamic environments such as intelligent vehicles, its practical deployment remains constrained by low signal‐to‐noise ratio (SNR) and susceptibility to motion artifacts. Here, we present an integrated in‐vehicle photoplethysmography (PPG) health monitoring system that overcomes these limitations through two synergistic innovations. First, a well‐developed, seamlessly integrated six‐sector MXene‐on‐Si photodetector array (SI‐PDs) with a high responsivity (∼0.72 A/W), paired with hardware‐level signal conditioning, enables high‐fidelity PPG acquisition under extremely weak signals. Second, a hardware‐accelerated multi‐scale convolutional neural network (CNN) performs robust, real‐time extraction of blood pressure (BP) and heart rate (HR). Experimental validation demonstrates reliable PPG capture from challenging body sites, with beat‐to‐beat BP prediction achieving R 2 >0.9 for both systolic and diastolic pressures, an FPGA core inference latency of 0.18 ms with an energy consumption of 0.059 mJ per inference. After the initial buffering, a sliding‐window strategy enables continuous prediction updates at a configurable refresh rate. Real‐vehicle tests at 40 and 80 km/h confirm the system's feasibility under moderate‐speed driving conditions. This work establishes a pathway toward high‐accuracy cardiovascular monitoring within intelligent vehicles, facilitating proactive health management and enhanced driving safety.