Sensor Selection for Tidal Volume Determination via Linear Regression-Impact of Lasso versus Ridge Regression.

Autor: Laufer B; Institute of Technical Medicine (ITeM), Furtwangen University, 78054 Villingen-Schwenningen, Germany., Docherty PD; Institute of Technical Medicine (ITeM), Furtwangen University, 78054 Villingen-Schwenningen, Germany.; Department of Mechanical Engineering, University of Canterbury, Christchurch 8041, New Zealand., Murray R; School of Mathematics and Statistics, University of Canterbury, Christchurch 8041, New Zealand., Krueger-Ziolek S; Institute of Technical Medicine (ITeM), Furtwangen University, 78054 Villingen-Schwenningen, Germany., Jalal NA; Institute of Technical Medicine (ITeM), Furtwangen University, 78054 Villingen-Schwenningen, Germany.; Innovation Center Computer Assisted Surgery (ICCAS), University of Leipzig, 04109 Leipzig, Germany., Hoeflinger F; Department of Microsystems Engineering, University of Freiburg, 79085 Freiburg, Germany., Rupitsch SJ; Department of Microsystems Engineering, University of Freiburg, 79085 Freiburg, Germany., Reindl L; Department of Microsystems Engineering, University of Freiburg, 79085 Freiburg, Germany., Moeller K; Institute of Technical Medicine (ITeM), Furtwangen University, 78054 Villingen-Schwenningen, Germany.; Department of Mechanical Engineering, University of Canterbury, Christchurch 8041, New Zealand.; Department of Microsystems Engineering, University of Freiburg, 79085 Freiburg, Germany.
Jazyk: angličtina
Zdroj: Sensors (Basel, Switzerland) [Sensors (Basel)] 2023 Aug 25; Vol. 23 (17). Date of Electronic Publication: 2023 Aug 25.
DOI: 10.3390/s23177407
Abstrakt: The measurement of respiratory volume based on upper body movements by means of a smart shirt is increasingly requested in medical applications. This research used upper body surface motions obtained by a motion capture system, and two regression methods to determine the optimal selection and placement of sensors on a smart shirt to recover respiratory parameters from benchmark spirometry values. The results of the two regression methods (Ridge regression and the least absolute shrinkage and selection operator (Lasso)) were compared. This work shows that the Lasso method offers advantages compared to the Ridge regression, as it provides sparse solutions and is more robust to outliers. However, both methods can be used in this application since they lead to a similar sensor subset with lower computational demand (from exponential effort for full exhaustive search down to the order of O ( n 2 )). A smart shirt for respiratory volume estimation could replace spirometry in some cases and would allow for a more convenient measurement of respiratory parameters in home care or hospital settings.
Databáze: MEDLINE
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