High‐performance piezoelectric yarns for artificial intelligence‐enabled wearable sensing and classification

Abstract Piezoelectric polymer fibers offer a fundamental element in intelligent fabrics with their shape adaptability and energy‐conversion capability for wearable activity and health monitoring applications. Nonetheless, realizing high‐performance smart polymer fibers faces a technical challenge due to the relatively low piezoelectric performance. Here, we demonstrate high‐performance piezoelectric yarns simultaneously equipped with structural robustness and mechanical flexibility. The key to substantially enhanced piezoelectric performance is promoting the electroactive β‐phase formation during electrospinning via adding an adequate amount of barium titanate (BaTiO3) nanoparticles into the poly(vinylidene fluoride‐trifluoroethylene) (P(VDF‐TrFE)). When transformed into a yarn structure by twisting the electrospun mats, the BaTiO3‐doped P(VDF‐TrFE) fibers become mechanically strengthened with significantly improved elastic modulus and ductility. Owing to the tailored convolution neural network algorithms architected for classification, the as‐developed BaTiO3‐doped piezo‐yarn device woven into a cotton fabric exhibits monitoring and identifying capabilities for body signals during seven human motion activities with a high accuracy of 99.6%