Flexible tactile neuromorphic devices are becoming important as the impetus for the development of human-machine collaboration. However, accomplishing and further transcending human intelligence with artificial intelligence still confront many barriers. Here, we present a self-powered stretchable three-dimensional remote tactile device (3D-RTD) that performs the depth-of-field (DOF) sensation of external mechanical motions through a conductive-dielectric heterogeneous structure. The device can build a logic relationship precisely between DOF motions of an external active object and sensory potential signals of bipolar sign, frequency, amplitude, etc. The sensory mechanism is revealed on the basis of the electrostatic theory and multiphysics modeling, and the performance is verified via an artificial-biological hybrid system with micro/macroscale interaction. The feasibility of the 3D-RTD as an obstacle-avoidance patch for the blind is systematically demonstrated with a rat. This work paves the way for multimodal neuromorphic device that transcends the function of a biological one toward a new modality for brain-like intelligence.

Liquid–solid triboelectric nanogenerators (L–S TENGs) can generate corresponding electrical signal responses through the contact separation of droplets and dielectrics and have a wide range of applications in energy harvesting and self-powered sensing. However, the contact between the droplet and the electret will cause the contact L–S TENG’s performance degradation or even failure. Here we report a noncontact triboelectric nanogenerator (NCLS-TENG) that can effectively sense droplet stimuli without contact with droplets and convert them into electrical energy or corresponding electrical signals. Since there is no contact between the droplet and the dielectric, it can continuously and stably generate a signal output. To verify the feasibility of NCLS-TENG, we demonstrate the modified murphy’s dropper as a smart infusion monitoring system. The smart infusion monitoring system can effectively identify information such as the type, concentration, and frequency of droplets. NCLS-TENG show great potential in smart medical, smart wearable and other fields.