Breathable Pulse Sensing Wristband With Full‐Module Shape Adaptivity for Machine Learning‐Assisted Continuous Blood Pressure Monitoring

ABSTRACT Continuous blood pressure (BP) monitoring remains a clinical imperative with the global prevalence of cardiovascular diseases. Despite the development of flexible electronics for on‐skin sensing of hemodynamic signals, the reliability of continuous BP tracking is still limited by the poor skin‐device coupling at anatomical concavities (e.g., wrist), which leads to motion artifacts and compromised long‐term wearability. Herein, we designed a breathable, full‐module shape‐adaptive pulse sensing wristband leveraging the foam structure of the shape memory polymer (SMP). The novel device demonstrates superior pressure sensing performance, achieving ultrahigh sensitivity (>2289.31 kPa −1 ) within a wide range (0–700 kPa), as well as ultralow detection limit (0.16 Pa) and response/recovery time (1.64 ms/3.42 ms), which enables the recording of arterial pulses in high fidelity. Through processing the pulse waveform with a machine learning‐based algorithm, continuous BP monitoring was achieved, with 97%/90% accuracy for systolic/diastolic BP (SBP/DBP) prediction, surpassing the clinical standard for BP measurement. Moreover, the wristband's full‐module breathability and the stress‐free skin‐sensor interface ensure long‐term wearability without skin irritation for up to 24 h. Significantly, the incorporation of SMPs dramatically optimize the bioelectronic interface for epidermal systems. And the as‐developed pulse sensing wristband holds great potential for population‐scale cardiovascular health management.