title: Comparison of a priori calibration models for respiratory inductance plethysmography during running.
abstract: Respiratory inductive plethysmography (RIP) has been introduced as an alternative for measuring ventilation by means of body surface displacement (diameter changes in rib cage and abdomen). Using a posteriori calibration, it has been shown that RIP may provide accurate measurements for ventilatory tidal volume under exercise conditions. Methods for a priori calibration would facilitate the application of RIP. Currently, to the best knowledge of the authors, none of the existing ambulant procedures for RIP calibration can be used a priori for valid subsequent measurements of ventilatory volume under exercise conditions. The purpose of this study is to develop and validate a priori calibration algorithms for ambulant application of RIP data recorded in running exercise. We calculated Volume Motion Coefficients (VMCs) using seven different models on resting data and compared the root mean squared error (RMSE) of each model applied on running data. Least squares approximation (LSQ) without offset of a two-degree-of-freedom model achieved the lowest RMSE value. In this work, we showed that a priori calibration of RIP exercise data is possible using VMCs calculated from 5 min resting phase where RIP and flowmeter measurements were performed simultaneously. The results demonstrate that RIP has the potential for usage in ambulant applications.
authors.forename: Heike
authors.initials: H
authors.lastname: Leutheuser
authors.forename: Christian
authors.initials: C
authors.lastname: Heyde
authors.forename: Albert
authors.initials: A
authors.lastname: Gollhofer
authors.forename: Bjoern M
authors.initials: BM
authors.lastname: Eskofier
mesh_terms: D000328:Adult; D002138:Calibration; D005260:Female; D045268:Flowmeters; D006801:Humans; D008297:Male; D008962:Models, Theoretical; D010991:Plethysmography / Q000379:methods*; D012044:Regression Analysis; D012119:Respiration; D012146:Rest; D012420:Running / Q000502:physiology*; D060388:Support Vector Machine