Screen-printed graphene-ink on a fitted sheet for pressure sensing and sleeping posture recognition by machine learning techniques

This paper presents the development and characterization of an innovative piezoresistive fabric-based sensor system, enhanced with graphene nanoplatelets (GNPs) for pressure sensing applications. Utilizing a screen-printing technique, an ink modified with GNPs is applied directly onto a crib-size fitted sheet (FS). The coated fabric underwent extensive characterization to evaluate its morphological, electrical, mechanical, and electromechanical properties. Results revealed a piezoresistive sensitivity (gauge factor) of approximately 9.9 in the wale direction and 1.2 in the course direction at an applied strain (ϵ) of 2.7%. Subsequently, data collected via an electrical resistance tomography (ERT)-inspired strategy were processed using machine learning (ML) classification techniques to detect pressure on the mattress covered with the piezoresistive coated fitted sheet (CFS). Experimental results demonstrate the proposed sensor system's capability for high-precision pressure sensing over large areas, achieving 96.3 % accuracy in recognizing compressed regions and 94.4% accuracy in identifying different sleeping positions on various zones of the sensorized mattress surface.