Document Detail


Improved regression models for ventilation estimation based on chest and abdomen movements.
MedLine Citation:
PMID:  22173273     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
Non-invasive estimation of minute ventilation is important for quantifying the intensity of physical activity of individuals. In this paper, several improved regression models are presented, based on the measurement of chest and abdomen movements from sensor belts worn by subjects (n = 50) engaged in 14 types of physical activity. Five linear models involving a combination of 11 features were developed, and the effects of different model training approaches and window sizes for computing the features were investigated. The performance of the models was evaluated using experimental data collected during the physical activity protocol. The predicted minute ventilation was compared to the criterion ventilation measured using a bidirectional digital volume transducer housed in a respiratory gas exchange system. The results indicate that the inclusion of breathing frequency and the use of percentile points instead of interdecile ranges over a 60 s window size reduced error by about 43%, when applied to the classical two-degrees-of-freedom model. The mean percentage error of the minute ventilation estimated for all the activities was below 7.5%, verifying reasonably good performance of the models and the applicability of the wearable sensing system for minute ventilation estimation during physical activity.
Authors:
Shaopeng Liu; Robert Gao; Qingbo He; John Staudenmayer; Patty Freedson
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Publication Detail:
Type:  Comparative Study; Journal Article; Research Support, N.I.H., Extramural    
Journal Detail:
Title:  Physiological measurement     Volume:  33     ISSN:  1361-6579     ISO Abbreviation:  Physiol Meas     Publication Date:  2012 Jan 
Date Detail:
Created Date:  2012-01-25     Completed Date:  2012-08-20     Revised Date:  2013-06-27    
Medline Journal Info:
Nlm Unique ID:  9306921     Medline TA:  Physiol Meas     Country:  England    
Other Details:
Languages:  eng     Pagination:  79-93     Citation Subset:  IM    
Affiliation:
Department of Mechanical Engineering, University of Connecticut, Storrs, CT, USA.
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MeSH Terms
Descriptor/Qualifier:
Abdomen / physiology*
Adult
Female
Humans
Male
Middle Aged
Models, Biological*
Movement / physiology*
Pulmonary Ventilation / physiology*
Regression Analysis
Thorax / physiology*
Young Adult
Grant Support
ID/Acronym/Agency:
U01 CA130783/CA/NCI NIH HHS; UO1 CA130783/CA/NCI NIH HHS
Comments/Corrections

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