Multi-touch points detection for capacitive touch panel

Abstract

Abstract-We consider the detection of multiple touch points for capacitive touch panel systems under Gaussian noise. We propose an algorithm that reduces the noise-induced detection error and improves the detection accuracy with partial touch signal information. The proposed algorithm is based on the likelihood ratio test, and utilizes the touch signal features, such as the local maximum, the range of touch magnitude, and the consecutive occurrence of touch locations, to first detect touch points and then calculates the real touch coordinates based on the weighting average technique. Simulation demonstrates the improved performance of the proposed algorithm.

P‐55: An Optimizing Finger Separation Method with Machine Learning Algorithm used In‐Cell Capacitive Touch Panel

“…In multi-touch devices, we need to recognize the number of fingers and their coordinates. T here have been numerous related studies in this field [1][2][3][4]. Generally, the distance of finger separation is about 2.3 times of the sensor pitch (1) which two fingers can be easily recognized, and the raw data is shown as figure 1.…”