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

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 (n2)). 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.

Automated summary

This research focuses on measuring respiratory volume using upper body movements and a smart shirt. By using motion capture and regression methods, the study determines the optimal selection and placement of sensors on the shirt to accurately recover respiratory parameters. The results show that the Lasso method outperforms Ridge regression, providing sparse solutions and better handling of outliers. The smart shirt could potentially replace spirometry and offer a more convenient way to measure respiratory parameters in home care or hospital settings.