Abstract
This paper presents a cost-effective pressure sensing system for object detection and identification. The pressure sensing system consists of a 27×27 piezoresistive sensor array made of carbon composite Velostat, a signal processing subsystem for signal scanning, amplification, registration, and enhancement. A convolutional neural network is used to classify various objects through the pressure signals produced and processed by the sensing array. Based on systematic characterizations and calibrations of sensing materials and system sensitivity, three experiment setups are established to recognize 10 objects to be detected. In series of experiments, a pressure image data set consisting of 32264 frames of images is first assembled to represent the 10 objects. Contrast enhancement algorithm was used to process the pressure image data set and combined with a convolutional neural network ResNet-PI to classify the 10 objects. For pressure images collected with the preestablished three experiment setups, an overall accuracy of 0.9854 is achieved. Compared with other systems based on Velostat sensor array, the system demonstrated in this study features improvements in structural robustness, detection repeatability and system reliability, suggesting its potential applications in emerging areas including human-computer interaction and smart health monitoring.
