A Touch Orientation Classification-Based Force–Voltage Responsivity Stabilization Method for Piezoelectric Force Sensing in Interactive Displays

Piezoelectric force touch panels have attracted a significant amount of interest in the field of human-machine interactivity, owing to its merits, such as a low power consumption, high force detection sensitivity and simple panel structure. However, the unstable force-voltage responsivity introduced by various touch orientations limits its successful use in interactive displays. To address this issue, in this article, we present a machine learning-based technique, in which a user finger touch-induced capacitive pattern is used to train an artificial neural network (ANN). Using this technique, a high touch angle classification accuracy (95.7%) and a high average force detection accuracy (90%) of distinct touch orientations are achieved. The presented technique prompts the development of piezoelectric force touch panels.