Neural networks for efficient touch detection on capacitive panels

Abstract

The advancement of capacitive-based touch panel technologies has opened new opportunities for their incorporation into embedded devices. However, this progress also underscores the need for improved software algorithms to achieve high precision in calculating touch coordinates. Traditional position calculation methods often exhibit diminished accuracy when applied to smaller panels, and modifying and tuning these methods can be time-consuming and labor-intensive. To address these limitations, this study investigates the performance of two neural network architectures, specifically a two-layer fully connected neural network and a radial basis function network, in enhancing the accuracy of touch coordinate calculation. A key advantage of these models is their ability to learn efficiently from limited datasets while minimizing the risk of overfitting. The high touch position accuracy achieved by the proposed neural network solutions makes them suitable for deployment in devices with limited computing resources, such as microcontrollers. Furthermore, the simplicity of the proposed models enables their implementation in embedded systems with low power consumption, offering a practical and scalable solution for a wide range of applications. Overall, the integration of these neural network models in touch coordinate processing provides notable benefits in terms of accuracy, efficiency, and adaptability.