<scp>High‐performance</scp> piezoelectric yarns for artificial intelligence‐enabled wearable sensing and classification

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