Estimation of Tidal Volume during Exercise Stress Test from Wearable-Device Measures of Heart Rate and Breathing Rate

Tidal volume (TV), defined as the amount of air that moves in or out of the lungs with each respiratory cycle, is important in evaluating the respiratory function. Although TV can be reliably measured in laboratory settings, this information is hardly obtainable under everyday living conditions. Und...

Full description

Saved in:
Bibliographic Details
Published in:Applied sciences 2022-05, Vol.12 (11), p.5441
Main Authors: Sbrollini, Agnese, Catena, Riccardo, Carbonari, Francesco, Bellini, Alessio, Sacchetti, Massimo, Burattini, Laura, Morettini, Micaela
Format: Article
Language:English
Subjects:
Age
Air flow
Body mass
Body mass index
Body size
Breathing
Cardiac stress tests
Confidence intervals
Electrocardiography
Heart rate
incremental test
indirect estimation
Instrumentation
Laboratories
Living conditions
Metabolism
Regression analysis
regression model
Respiration
respiratory frequency
Respiratory function
Spirometry
Wearable computers
Wearable technology
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Tidal volume (TV), defined as the amount of air that moves in or out of the lungs with each respiratory cycle, is important in evaluating the respiratory function. Although TV can be reliably measured in laboratory settings, this information is hardly obtainable under everyday living conditions. Under such conditions, wearable devices could provide valuable support to monitor vital signs, such as heart rate (HR) and breathing rate (BR). The aim of this study was to develop a model to estimate TV from wearable-device measures of HR and BR during exercise. HR and BR were acquired through the Zephyr Bioharness 3.0 wearable device in nine subjects performing incremental cycling tests. For each subject, TV during exercise was obtained with a metabolic cart (Cosmed). A stepwise regression algorithm was used to create the model using as possible predictors HR, BR, age, and body mass index; the model was then validated using a leave-one-subject-out cross-validation procedure. The performance of the model was evaluated using the explained variance (R2), obtaining values ranging from 0.65 to 0.72. The proposed model is a valid method for TV estimation with wearable devices and can be considered not subject-specific and not instrumentation-specific.
ISSN:2076-3417
2076-3417
DOI:10.3390/app12115441