Machine Learning‐Augmented Piezoelectric‐Ferroelectret Nanogenerators for Highly Sensitive Respiration Monitoring in Wearable Healthcare

ABSTRACT The precise detection of low‐amplitude and spatially variant biomechanical signals continues to present a substantial technological challenge. Ferroelectrets, with their exceptional piezoelectric coefficients and dynamic response characteristics, represent a compelling alternative for developing high‐sensitivity pressure sensors and deformable energy harvesting systems. Herein, we present a highly sensitive poly(vinylidene fluoride) (PVDF) ferroelectret film featuring a cross‐scale pore structure fabricated using organic dimethylhexanediol (DMHD) crystals as sacrificial templates, sandwiched between two PVDF layers, thus forming a piezoelectric‐ferroelectret‐ piezoelectric (PFP) three‐layer thin film. The engineered porous architecture enables the substantial net charge storage and creates an oriented space charge network, where subtle mechanical loading (<1 N) induces pronounced charge displacements that generate strong local electric field variations. As a result, the optimized PEP devices exhibit a superior piezoelectric coefficient of 650 pC·N −1 , and high sensitivity of 595.85 mV·kPa −1 . As a proof‐of‐concept, the PFP device is seamlessly integrated into a facial mask, facilitating accurate recognition of respiratory behaviors. With the assistance of both a Convolutional Neural Network (CNN) and a Bidirectional Long Short‐Term Memory network (BiLSTM), a PEP‐based smart mask can recognize respiratory tracts and multiple breathing patterns with a classification accuracy of up to 100%. This study pioneers a high‐efficacy respiratory monitoring device via ferroelectret ultra‐sensitivity, showing transformative prospects for clinical diagnostics and daily healthcare implementation.