Highlights

• Carbon-based gradient resistance element structure is proposed for the construction of multifunctional touch sensor, which will promote wide detection and recognition range of multiple mechanical stimulations.

• Multifunctional touch sensor with gradient resistance element and two electrodes is demonstrated to eliminate signals crosstalk and prevent interference during position sensing for human–machine interactions.

• Biological sensing interface based on a deep-learning-assisted all-in-one multipoint touch sensor enables users to efficiently interact with virtual world.

Abstract

Human–machine interactions using deep-learning methods are important in the research of virtual reality, augmented reality, and metaverse. Such research remains challenging as current interactive sensing interfaces for single-point or multipoint touch input are trapped by massive crossover electrodes, signal crosstalk, propagation delay, and demanding configuration requirements. Here, an all-in-one multipoint touch sensor (AIOM touch sensor) with only two electrodes is reported. The AIOM touch sensor is efficiently constructed by gradient resistance elements, which can highly adapt to diverse application-dependent configurations. Combined with deep learning method, the AIOM touch sensor can be utilized to recognize, learn, and memorize human–machine interactions. A biometric verification system is built based on the AIOM touch sensor, which achieves a high identification accuracy of over 98% and offers a promising hybrid cyber security against password leaking. Diversiform human–machine interactions, including freely playing piano music and programmatically controlling a drone, demonstrate the high stability, rapid response time, and excellent spatiotemporally dynamic resolution of the AIOM touch sensor, which will promote significant development of interactive sensing interfaces between fingertips and virtual objects.

中文翻译：

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一种具有碳基梯度电阻元件的一体化多功能触摸传感器

强调

• 提出了碳基梯度电阻元件结构用于构建多功能触摸传感器，这将促进多种机械刺激的广泛检测和识别范围。

• 具有梯度电阻元件和两个电极的多功能触摸传感器被证明可以消除信号串扰并防止人机交互位置感测期间的干扰。

• 基于深度学习辅助一体式多点触摸传感器的生物传感界面使用户能够与虚拟世界高效交互。

抽象的

使用深度学习方法的人机交互对于虚拟现实、增强现实和元宇宙的研究非常重要。此类研究仍然具有挑战性，因为当前用于单点或多点触摸输入的交互式传感接口受到大量交叉电极、信号串扰、传播延迟和苛刻配置要求的限制。这里，报道了一种仅具有两个电极的一体化多点触摸传感器（AIOM触摸传感器）。AIOM 触摸传感器由梯度电阻元件高效构建，可以高度适应各种依赖于应用的配置。结合深度学习方法，AIOM触摸传感器可用于识别、学习和记忆人机交互。基于AIOM触摸传感器构建的生物识别验证系统，可实现超过98%的高识别准确率，并提供有前景的混合网络安全防止密码泄露。多样化的人机交互，包括自由演奏钢琴音乐和编程控制无人机，展示了AIOM触摸传感器的高稳定性、快速响应时间和出色的时空动态分辨率，这将促进指尖和虚拟之间的交互式传感接口的重大发展对象。