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.

Incorporating Wireless Strategies to Wearable Devices Enabled by a Photocurable Hydrogel for Monitoring Pressure Information.

Advances in emerging technologies for wireless collection and the timely analysis of various information captured by wearable devices are of growing interest. Herein, a crosslinked ionic hydrogel prepared by a facile photocuring process is proposed, which allows wearable devices to be further incorporated into two wireless integrated systems for pressure monitoring applications. The device exhibits a simplified structure by effectively sharing functional layers, rather than conventional two separate combinations, offering the salient performance of iontronic sensing and electrochromic properties to simultaneously quantify and visualize pressure. The developed smart patch system is demonstrated to monitor physiological signals in real-time utilizing the user interface of remote portable equipment with the Bluetooth protocol and on-site electrochromic displays. Moreover, a passive wireless system based on the magnetic coupling effect is designed, which can operate free from the battery and simultaneously acquire multiple pressure information. It is envisioned that the strategies would hold enormous potential for flexible electronics, versatile sensing platforms, and wireless on-body networks.

Advances in Emerging Photonic Memristive and Memristive-Like Devices.

Possessing the merits of high efficiency, low consumption, and versatility, emerging photonic memristive and memristive-like devices exhibit an attractive future in constructing novel neuromorphic computing and miniaturized bionic electronic system. Recently, the potential of various emerging materials and structures for photonic memristive and memristive-like devices has attracted tremendous research efforts, generating various novel theories, mechanisms, and applications. Limited by the ambiguity of the mechanism and the reliability of the material, the development and commercialization of such devices are still rare and in their infancy. Therefore, a detailed and systematic review of photonic memristive and memristive-like devices is needed to further promote its development. In this review, the resistive switching mechanisms of photonic memristive and memristive-like devices are first elaborated. Then, a systematic investigation of the active materials, which induce a pivotal influence in the overall performance of photonic memristive and memristive-like devices, is highlighted and evaluated in various indicators. Finally, the recent advanced applications are summarized and discussed. In a word, it is believed that this review provides an extensive impact on many fields of photonic memristive and memristive-like devices, and lay a foundation for academic research and commercial applications.

Anodized Aluminum Oxide-Assisted Low-Cost Flexible Capacitive Pressure Sensors Based on Double-Sided Nanopillars by a Facile Fabrication Method.

Flexible pressure sensors have garnered enormous attention in recent years as they hold great promise in wearable electronic devices. However, the realization of a high-performance flexible pressure sensor via a facile and cost-effective approach still remains a challenge. In this work, a capacitive pressure sensor based on a poly(vinylidenefluoride-co-trifluoroethylene) [P(VDF-TrFE)] dielectric film that incorporates nanopillars into both sides is demonstrated. Unlike the previous complicated and expensive methods, large-scale regular and uniform nanopillars are easily and economically achieved by the pattern transfer of anodized aluminum oxide templates. The double-sided nanopillars constituting the P(VDF-TrFE) dielectric layer enable the pressure sensor with high sensitivity (∼0.35 kPa-1), wide working range (4 Pa to 25 kPa), short response time (∼48 ms), and excellent durability. In addition to these salient features, our sensor also exhibits superior performances under bending states, ensuring that it can be used for detecting diverse practical stimuli as experimentally validated by perceiving real-time and in-site human physiological signals and body motions that, respectively, correspond to the low- and high-pressure range. A sensor array is finally constructed and is shown to be capable of perceiving the spatial pressure distribution of either a contact or noncontact object. These demonstrations show a promising future of our sensor in healthcare monitoring, smart robot skin, and human-machine interfaces.