A Fully Integrated Patch for Real‐Time AI‐Enhanced Haptic Closed‐Loop Interaction of Complete 128 ASCII Codes

Abstract The development of human–computer interaction technology is directly linked to economic development, serving as a crucial threshold that enables information technology to integrate into society. However, current emerging HCI faces challenges with not supporting complete 128 ASCII codes and insufficient interaction feedback. Here, a wireless, real‐time and AI‐enhanced haptic HCI system based on a conformal skin patch is proposed, supporting closed‐loop interaction of complete 128 ASCII codes. The patch leverages the outstanding electrical performance of haptic sensor and actuator, the mechanical properties of flexible serpentine circuits, and the efficient processing of advanced AI algorithms. Through bionic fingerprint electrode and in situ grown fibrous microstructure, the haptic sensor achieves high sensitivity of up to 997.2 kPa −1 . Small vibration actuator modulated by PWM can provide 7 levels of haptic feedback, achieving 91% distinction accuracy of individual array elements on skin. To avoid the cumbersome process of data collection, a sensor array encoding rule is defined capable of covering complete 128 ASCII codes, and mathematical modeling simulates pressing behaviors and efficiently synthesizes large‐scale dataset. Advanced CNN deep learning, trained on the massive dataset, successfully classifies complete 128 ASCII codes, including complex characters like “G,” “o,” “!” and “<”.