3-D Printed Graphene-Based Piezoresistive Foam Mat for Pressure Detection Through Electrical Resistance Tomography and Machine Learning Classification Techniques

This letter presents the development and investigation of a novel soft piezoresistive foam mat sensor (FMS) based on graphene nanoplatelets (GNPs) for pressure sensing. The sensor is fabricated using a 3-D printed polyvinyl alcohol (PVA) water-soluble sacrificial template, which is then infiltrated with Ecoflex polymer and dip-coated in a GNP-ethanol solution. The mechanical response as well as the high piezoresistive sensitivity (0.31 kPa
⁻¹
@ 8 kPa) of the fabricated foam is assessed experimentally. Pressure detection is achieved through electrical resistance tomography (ERT) using the opposite current injection method, and the collected data are processed using machine learning (ML) classification techniques to localize pressure application on the FMS's surface. The experimental results demonstrate the potential of the suggested approach to effectively detect pressure across extensive surface areas, achieving an accuracy of approximately 87.5% or 83.7%, respectively, for identifying the presence of deformation resulting from a single fingertip touch or from the simultaneous touch of two fingers at separate points on different zones of the FMS.