Proceedings on Automation in Medical Engineering
Vol. 2 No. 1 (2023): Proc AUTOMED

Measurement technology and diagnostics, ID 734

Sensor selection for tidal volume determination via regression – proof of methodology

Main Article Content

Bernhard Laufer (Institute of Technical Medicine ITeM), Nour Aldeen Jalal (Institute of Technical Medicine (ITeM), Furtwangen University, Villingen-Schwenningen, Germany), Paul David Docherty  (Institute of Technical Medicine (ITeM), Furtwangen University, Villingen-Schwenningen, Germany), Sabine Krueger-Ziolek  (Institute of Technical Medicine (ITeM), Furtwangen University, Villingen-Schwenningen, Germany), Fabian Hoeflinger (Department of Microsystems Engineering, University of Freiburg, Freiburg, Germany), Leonhard Reindl  (Department of Microsystems Engineering, University of Freiburg, Freiburg, Germany), Knut Moeller (Institute of Technical Medicine (ITeM), Furtwangen University, Villingen-Schwenningen, Germany)

Abstract

Measurement of respiratory volumes based on breath-related upper body movements continues to be a subject of interest in science and research. In general, smart garments are becoming more common in medical diagnostics and therapy monitoring, and improved, miniaturized and more accurate sensors are opening up new opportunities. A crucial issue in the development of smart clothing is how many sensors to use and where to place them in the clothes. Using data from a motion capture system, two different regression methods (Lasso and Ridge) were evaluated that can be used to select appropriate sensor subsets. The performance of the subsets, obtained by the regression methods, were compared with the best set of sensors obtained by analysing all possible subsets. The Lasso method showed clear performance advantages over Ridge regression in this field of application, but both methods can be employed as they significantly reduce time and computational effort.

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