An All-In-One Multifunctional Touch Sensor with Carbon-Based Gradient Resistance Elements

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.

Automated summary

This article reports an all-in-one multipoint touch sensor with only two electrodes, which is efficiently constructed by gradient resistance elements. Combined with deep learning method, the sensor enables recognition, learning, and memory of human-machine interactions. It also demonstrates high stability, rapid response time, and excellent spatiotemporally dynamic resolution in various interactions, showing promising applications in biometric verification and cybersecurity.