类安全气囊梳状柔性压力传感器及其可穿戴应用

IF 8.2 2区 材料科学 Q1 MATERIALS SCIENCE, MULTIDISCIPLINARY
Yingxi Xie, Sheng Bian, Longsheng Lu, Hanxian Chen, Jiayue Liao, Yuxuan Liang, Xiaohua Wu
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引用次数: 0

摘要

柔性可穿戴设备在医疗保健和人机交互方面显示出巨大的潜力,然而为这些应用开发高性能柔性压力传感器仍然是一个紧迫的技术挑战。受安全气囊梳子结构的启发,采用激光直写(LDW)技术,设计并制造了一种类似安全气囊梳子的柔性压力传感器(ALCS)。通过采用安全气囊结构来增强传感层和电极层之间的接触面积变化,再加上孔设计来进一步提高单位应变,ALCS实现了超宽的检测范围(1.27-2783.815 kPa),高灵敏度(高达21.53 kPa - 1),以及极快的响应/恢复时间(3.4 ms/34 ms)。为解决动态手语识别系统缺乏的问题,我们制作了一种基于ALCS的智能手套(ALCIG),结合机器学习算法,实现了对8种动态手语的精确识别,为语言障碍人士提供了一种高效的无障碍交流解决方案。为了进一步评估ALCIG的识别能力,提高其在不同场景下的适用性,我们开发了一个包含26个类别的虚拟键盘字母识别测试。结果表明,即使在26个高度复杂的目标中,ALCIG也能成功地收集数据并实现准确分类,显示了其在手势识别和复杂分类任务中的巨大潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Airbag-like Comb Flexible Pressure Sensor and Its Wearable Applications

Airbag-like Comb Flexible Pressure Sensor and Its Wearable Applications
Flexible wearable devices demonstrate immense potential in healthcare and human–computer interaction, yet the development of high-performance flexible pressure sensors for these applications remains a pressing technical challenge. Inspired by the structure of commonly used airbag combs, an airbag-like comb flexible pressure sensor (ALCS) was designed and fabricated using laser direct writing (LDW) technology. By incorporating an airbag structure to enhance the variation in contact area between the sensing and electrode layers, coupled with pore design to further boost unit strain, the ALCS achieved an ultrawide detection range (1.27–2783.815 kPa), high sensitivity (up to 21.53 kPa–1), and exceptionally fast response/recovery times (3.4 ms/34 ms). To tackle the issue of lacking dynamic sign language recognition systems, we fabricated an intelligent glove (ALCIG) based on ALCS, which, when integrated with machine learning algorithms, achieved precise recognition of 8 types of dynamic sign language, offering an efficient solution for barrier-free communication for individuals with speech impairments. To further evaluate the recognition capabilities of ALCIG and enhance its applicability in diverse scenarios, we developed a virtual keyboard letter recognition test involving 26 categories. The results demonstrated that even with 26 highly complex targets, ALCIG successfully collected data and achieved accurate classification, showcasing its significant potential in gesture recognition and complex classification tasks.
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来源期刊
ACS Applied Materials & Interfaces
ACS Applied Materials & Interfaces 工程技术-材料科学:综合
CiteScore
16.00
自引率
6.30%
发文量
4978
审稿时长
1.8 months
期刊介绍: ACS Applied Materials & Interfaces is a leading interdisciplinary journal that brings together chemists, engineers, physicists, and biologists to explore the development and utilization of newly-discovered materials and interfacial processes for specific applications. Our journal has experienced remarkable growth since its establishment in 2009, both in terms of the number of articles published and the impact of the research showcased. We are proud to foster a truly global community, with the majority of published articles originating from outside the United States, reflecting the rapid growth of applied research worldwide.
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