An Evolutionary Calibration Approach for Touch Interface Filter Chains

Abstract

Touch interfaces are human machine interface (HMI) that can be found in a wide range of products ranging from mobile phones over cars to home appliances. Many of these HMIs measure digital signals which are used to detect touch events. These signals are processed using filters in order to decide whether there is a touch event or not. The filterchain must be functional even if the signal contains heavy noise. Thus a precise calibration of the individual filters is necessary. We employ a genetic algorithm (GA) to choose the filter parameters automatically. We evaluate our approach in a series of experiments which includes simulated as well as real data. We additionally compare our GA with manually calibrated parameters and thereby show the superiority of our method in terms of the accuracy of the calibration provided. A cost-intensive manual calibration can thus be avoided.