Rectified Artificial Neural Networks for Long-Term Force Sensing in Piezoelectric Touch Panels

Human–machine interfaces based on force touch panels have attracted enormous attention due to the merits of the high human–machine interaction efficiency. Many studies have been devoted to diverse force touch technologies. Broad applications in terms of both actual use and research have been developed, such as 3D touch and force-based keystroke authentication. The fruitful results are based on the assumption that users’ touch habits remain unchanged over time; thus, a stationary customized force-sensing model can be built. However, for long-term use, users’ touch habits change due to time-drifting and specific events, causing a decrease in the performance of stationary force-sensing models. To address this issue, a rectified artificial neural network for long-term force sensing in piezoelectric touch panels is presented in this paper. With additional information on the touching time and the occurrence of specific events, the force level predictions were rectified, achieving an accuracy of 97.62% for a long-term data set. The proposed technique enables customized force sensing for long-term use and enhances the human–machine interactive efficiency.