Recently, Professor Che Lufeng's research group from the College of Information Science and Electronic Engineering at Zhejiang University has made a significant leap forward in wearable patch technology. Their innovative work has led to the creation of a fully integrated, stretchable, and AI-driven wearable patch. This patch facilitates real-time haptic closed-loop human-machine interaction, encompassing the entire set of 128 ASCII codes. The study, named 'A Fully Integrated Patch for Real-Time AI-Enhanced Haptic Closed-Loop Interaction of Complete 128 ASCII Codes,' was featured in Advanced Functional Materials, a leading international journal in materials science.

The patch is built upon haptic sensors and vibration actuators. It incorporates bio-inspired fingerprint electrodes and in-situ grown fibrous microstructures, resulting in a haptic sensor with an impressive sensitivity of 997.2 kPa⁻¹. A PWM-modulated micro-vibration actuator offers seven distinct levels of haptic feedback, achieving a remarkable 91% accuracy in distinguishing individual touch points on the skin.

The research team also tackled the traditional dependence on manually repeated scenarios. They did this by encoding sensor array press counts and constructing a mathematical model to swiftly generate extensive training datasets. The CNN model, trained on this data, is capable of classifying 128 ASCII codes in real-time. When integrated with software development, the system enables real-time haptic feedback in VR games, enhancing the immersive experience and reducing visual fatigue.

This pioneering research has garnered substantial support from the Key Laboratory Project of Advanced Micro-Nano Sensing Technology in Zhejiang Province.