A Smart Pen Based on Triboelectric Effects for Handwriting Pattern Tracking and Biometric Identification

A Rapid Adaptation Approach for Dynamic Air-Writing Recognition Using Wearable Wristbands with Self-Supervised Contrastive Learning

Wearable wristband systems leverage deep learning to revolutionize hand gesture recognition in daily activities. Unlike existing approaches that often focus on static gestures and require extensive labeled data, the proposed wearable wristband with self-supervised contrastive learning excels at dynamic motion tracking and adapts rapidly across multiple scenarios. It features a four-channel sensing array composed of an ionic hydrogel with hierarchical microcone structures and ultrathin flexible electrodes, resulting in high-sensitivity capacitance output. Through wireless transmission from a Wi-Fi module, the proposed algorithm learns latent features from the unlabeled signals of random wrist movements. Remarkably, only few-shot labeled data are sufficient for fine-tuning the model, enabling rapid adaptation to various tasks. The system achieves a high accuracy of 94.9% in different scenarios, including the prediction of eight-direction commands, and air-writing of all numbers and letters. The proposed method facilitates smooth transitions between multiple tasks without the need for modifying the structure or undergoing extensive task-specific training. Its utility has been further extended to enhance human-machine interaction over digital platforms, such as game controls, calculators, and three-language login systems, offering users a natural and intuitive way of communication.

A Triboelectric Sensor with Double Bubble Structure Applied in a High Security Double Lock System

With more attention on personal privacy and the need for a security defense, it is necessary to design an intelligent lock system with a higher security performance. Here, a novel high security double lock system integrating triboelectric nanogenerators (TENGs) with a double bubble structure (DB-TENG) and deep learning models is proposed. The TENG as a self-powered sensor is developed using silicone rubber and copper foil. By optimizing the thickness of the top layer film, surface microstructure, the size of the air bubble, and design of the double bubble structure, the sensitivity of the DB-TENG reaches 19.08 V/kPa. For the feasibility study, the sensor is fabricated to a smart belt to collect respiratory behaviors as a respiratory code. A Long Short-Term Memory network is adopted to identify four typical respiratory signals with an average accuracy of 97.00%. The system is deployed on a Raspberry Pi to determine whether the user is permitted through both the collected respiratory code and the related face image and will send an alarm message if one of the two does not match. It is worth mentioning that users can send alarm signals undiscovered by controlling their respiratory signals. Therefore, the proposed system has superb potential in security demanding environments.