Evolutionary Touch Filter Chain Calibration

• Published in: SN computer science 2022-10, Vol.4 (1), p.26, Article 26
• Main Authors: Gerber, Daniel, Rosenbauer, Lukas, Lindner, Pia, Maier, Johannes, Stein, Anthony, Hähner, Jörg
• Format: Article
• Language: English
• Subjects: Automation; Automation and Robotics; Calibration; Computer Imaging; Computer Science; Computer Systems Organization and Communication Networks; Data Structures and Information Theory; Electronics; Embedded systems; Evolutionary algorithms; Genetic algorithms; Household appliances; Informatics in Control; Information Systems and Communication Service; Knobs; Man-machine interfaces; Neural networks; Noise control; Optimization algorithms; Original Research; Pattern Recognition and Graphics; Qualitative analysis; Signal processing; Software; Software Engineering/Programming and Operating Systems; Vision
• ISSN: 2661-8907; 2662-995X; 2661-8907
• DOI: 10.1007/s42979-022-01375-8

Human machine interfaces (HMIs) are an essential part of our daily lifes. Generally, a shift from mechanical HMIs such as knobs to more electronics-based solutions can be observed. A well-known example are touch interfaces which may be found in a variety of products ranging from phones over cars to home appliances. Touch interfaces offer more usability but pose increased technical challenges which must be faced by the development team in order to meet customer demands. Additionally, legal obligations regarding their robustness must be fullfilled (even under noise) in order to allow the novel products to enter the market. This is often enabled by employing a dedicated signal filter chain which is to be set accordingly. The corresponding calibration process is often time intensive and error-prone if performed manually. This is for example the case for our industrial partner which is a major producer in the consumer electronics market. This work analyses if the pain of a manual calibration can be resolved. This task consists of probing new calibration(s) and measuring their performance as well as choosing a calibration. The former is automated by developing a corresponding testbed which can simulate various noise classes and can perform touch interactions on a real user interface using a six-axis robot. The novel calibrations are determined by a genetic algorithm (GA) which is a special optimizer out of the family of evolutionary algorithms. The optimization is the main focus of this study. Quantitatively the GA was analysed by benchmarking it successfully against several other optimizers as well as against a human expert. Furthermore, the search time of the GA could be reduced by improving the stopping criterion. The analysis is rounded up by a qualitative analysis which showed that the GA’s calibrations can withstand noise to a certain degree whilst still identifying touch events if and only if they occurred. Over all the study outlined that an automation of the manual process is possible on the scenarios considered.