A Deep Learning Enabled Sustainable Self‐Powered Wearable Tactile Sensor toward Gesture Recognition

ABSTRACT Flexible and wearable piezoelectric sensors have gained attention for applications in human‐machine interfacing (HMI) and the artificial intelligence of things (AIoT). In this study, antimony‐doped barium titanate (Ba 0.3 Sb₀.₇TiO 3 ) was used to fabricate a piezoelectric nanogenerator (PENG) for finger movement sensing and gesture recognition. The polymer‐to‐particle ratio was optimized, with 20 wt.% yielding the best performance. The optimized PENG generated a maximum output of 50 V and 1.5 µA under applied force, validated by charging commercial capacitors and powering LEDs. For real‐time applications, the device was scaled to finger size and integrated into a wearable glove capable of detecting finger motion. The generated electrical signals were processed using convolutional neural networks (CNNs), converting the signals into readable text through deep learning. Using this approach, the glove successfully recognized the words “SOS” and “HELLO,” demonstrating the potential of the PENG for smart wearable applications. This work highlights the integration of piezoelectric sensing with AI‐enabled gesture recognition, offering a promising route for advanced wearable healthcare devices and interactive technologies.