Efficient Sensor Selection for Individualized Prediction Based on Biosignals - Vieth, Markus; Grimmelsmann, Nils; Schneider, Axel; Hammer, Barbara; Yin, Hujun; Camacho, David; Tino, Peter; 23rd International Conference on Intelligent Data Engineering and Automated Learning; CareTech OWL - Zentrum für Gesundheit, Soziales und Technologie; TransCareTech - Transformation in Care & Technology; Institut für Systemdynamik und Mechatronik (Intelligent Data Engineering and Automated Learning – IDEAL 2022. 23rd International Conference, IDEAL 2022, Manchester, UK, November 24–26, 2022, Proceedings, 2022)
Soft sensors combine a hardware component with an intelligent algorithmic processing of the raw sensor signals. While individualization of software components according to a person’s specific needs is comparably cheap, individualization of the sensor hardware itself is usually impossible in mass production. At the same time, the number of raw sensors should be minimum to reduce production costs. In this contribution, we propose to model this challenge as a feature selection problem, which optimizes a feature set simultaneously with respect to a family of functions corresponding to individualized post-processing of sensor signals. This concept is integrated into a number of different classical feature selection schemes, and evaluated in the context of the placement of pressure sensors as part of a shoe insole. It turns out that feature selection respecting the class of functions is superior to both placement based on anatomical considerations and classical feature selection methods.
published
yes
Keywords: conference paper