Monitoring of pulse signals with flexible paper-based graphene sensor

Abstract

Wearable flexible sensors are being increasingly designed for monitoring human health states. In this study, we optimized the preparation process of paper-based graphene and designed amplifier filter circuit to sense weak pulse signals with high sensitivity (0~300Pa) and fast response time (<1ms). We further used a convolutional neural network (CNN) as the recognition method to analyze the collected pulse signals. We successfully achieved the recognition and classification of three signals (Cun, guan, chi, and Sport) with an accuracy of 85.79% in the training set and an accuracy of 80% in the testing set. Our research offers new possibilities for the wide application of paper-based graphene sensors in medical diagnosis and health monitoring.