A self-powered corrugated paper structure triboelectric sensor with deep learning for table tennis motion...

Intelligent sports equipment has attracted increasing attention for quantitative performance evaluation and personalized training. Here, a corrugated paper-based triboelectric nanogenerator (CP-TENG) is developed for mechanical energy harvesting and table tennis motion sensing, combined with artificial intelligence-assisted signal recognition. A dielectric enhancement strategy is implemented using poly(vinylidene fluoride)/lead zirconate titanate (Pb(Zr,Ti)O₃) (PVDF/PZT) composite films as the negative triboelectric layer, paired with a roughened copper positive layer. Compared with pure PVDF-based devices, the CP-TENG exhibits enhanced electrical outputs, with increases of ∼36% in open-circuit voltage (V_OC), ∼37% in short-circuit current (I_SC), and ∼29% in transferred charge (Q_SC). Output coupling through rational interconnection of multiple units further delivers stable signals of ∼46.8 V, ∼15.1 μA, and ∼34.9 nC. Beyond energy harvesting, a self-powered CP-TENG sensor array integrated into a table tennis racket enables spatially resolved impact sensing and joint motion tracking. Combined with artificial intelligence algorithms, reliable volunteer identification is achieved with an accuracy of 94.5%, demonstrating a promising approach for intelligent sports monitoring.