Force Estimation With Sustainable Hydroxypropyl Cellulose Sensor Using Convolutional Neural Network

This article presents the development and analysis of a hydroxypropyl cellulose (HPC)-based sensor for force estimation. The sensor is based on the mixture between the HPC and deionized water with different concentrations. The red-green-blue (RGB) components of the sensors responses are analyzed as a function of the concentration of HPC, which indicate the relation between the stable color and the concentration. In addition, the experiments with the force variation on the sensor system indicate the correlation between the concentration of the HPC and the sensor performance, where the sample with 63% concentration (in weight) demonstrated a higher sensitivity for red and green components. It is also worth noting that there is the possibility of measuring the force distribution along the HPC sample, where the effect of camera illumination is also analyzed, where an increase on sensor sensitivity is obtained in all analyzed cases. Furthermore, the analysis of sensitivity variation along the HPC sensor is performed by applying forces at different positions on the HPC sensor, where it is possible to observe a higher uniformity on the sample with 57% concentration. Such sample is used on the 2-D shape reconstruction of the device for measuring the force distribution along the sample, which demonstrated the feasibility of the proposed device on force distribution assessment with sub-centimeter spatial resolution. Finally, the use of a convolutional neural network (CNN) for image processing is implemented to increase the accuracy of the proposed device, which resulted in an average mean squared error (mse) of 0.037.