High-Performance and Low-Complexity Multitouch Detection for Variable Ground States

Abstract

Multitouch detection algorithms are crucial for precise interaction with touch screens. However, as existing multitouch detection algorithms only target the good ground mass (GGM) environment, performance drops sharply when the ground state is unstable, such as the low ground mass (LGM) environment. This article introduces an enhanced multitouch detection algorithm tailored for both ground environments, addressing deficiencies in recognizing large touch areas and precise coordinates. First, the center of thumb search (CTS) method combined with an adaptive valley point division (VPD) skip process for large circular touches, such as thumb touches, enables detection without excessive segmentation. Second, conditional VPD thresholding is designed to distinguish similar single and multitouch in LGM environment. This algorithm posed challenges that negatively impacted the detection performance in the GGM environment; however, these issues were addressed through the development of ground state classifier (GSC). At last, CTS algorithm facilitates the distinction of the center of a large-sized thumb touch, enhancing the resolution in closely spaced touch scenarios by properly partitioning touch groups. Experimental results demonstrate significant improvements in accuracy and linearity. We have quantitatively confirmed substantial enhancements in performance from a user standpoint, achieving 96.07% in accuracy for the total dataset and $\times 13.36$ better in linearity. These innovations collectively advance the state of touch detection technology in challenging LGM environments, presenting a robust framework for future applications.