Abstract
This paper proposes an innovative pressure imaging approach that utilizes copper foil grids and electromagnetic field variations under different excitation frequencies and conditions, coupled with deep learning algorithms for interpreting voltage signals. The sensor design enables an adjustable pressure detection range along with features such as a concise structure, simple wiring, easy fabrication, and scalability for large-area detection. Multiple excitation frequencies are employed to alter the pattern of the electromagnetic field, facilitating the capture of more comprehensive information and augmenting the dimensionality of the input data, thereby improving the model's capacity to interpret voltage signals. The proposed approach is validated through simulation studies, demonstrating generally accurate imaging of pressure distributions, and proving the concept. In simulated environments, while minor disparities between the predicted and true pressure imaging indicate room for future refinement, the promising outcomes and a mean squared error of 0.102 in validation, with pressure scaled from 0 to 1, affirm the approach's potential.
