Bimodal Intelligent Electronic Skin Based on Proximity and Tactile Interaction for Pressure and Configuration Perception

Abstract

The flexible bimodal e-skin exhibits significant promise for integration into the next iteration of human−computer interactions, owing to the integration of tactile and proximity perception. However, those challenges, such as low tactile sensitivity, complex fabrication processes, and incompatibility with bimodal interactions, have restricted the widespread adoption of bimodal e-skin. Herein, a bimodal capacitive e-skin capable of simultaneous tactile and proximity sensing has been developed. The entire process eliminates intricate fabrication techniques, employing DLP-3D printing for the electrode layers and sacrificial templating for the dielectric layers, conferring high tactile sensitivity (1.672 kPa −1 ) and rapid response capability (∼30 ms) to the bimodal e-skin. Moreover, exploiting the "fringing electric field" effect inherent in parallel-plate capacitors has facilitated touchless sensing, thereby enabling static distance recognition and dynamic gesture recognition of varying materials. Interestingly, an e-skin sensing array was created to identify the positions and pressure levels of various objects of different masses. Furthermore, with the aid of machine learning techniques, an artificial neural network has been established to possess intelligent object recognition capabilities, facilitating the identification, classification, and training of various object configurations. The advantages of the bimodal e-skin render it highly promising for extensive applications in the field of next-generation human−machine interaction.

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