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,propaga-tion delay,and demanding configuration requirements.Here,an all-in-one multipoint touch sensor(AIOM touch sensor)with only two elec-trodes 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 program-matically 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.