http://iet.metastore.ingenta.com
1887

Classification of Doppler radar reflections as preprocessing for breathing rate monitoring

Classification of Doppler radar reflections as preprocessing for breathing rate monitoring

For access to this article, please select a purchase option:

Buy eFirst article PDF
£12.50
(plus tax if applicable)
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Signal Processing — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

Classification is presented as a pre-processing step in this study. The state of the subject is classified as the unmoving state with normal breathing (normal breathing class), unmoving state with no breathing (stop breathing class) or the state when the subject is moving (erratic signal class) before breathing estimation algorithms are applied. Estimation algorithms may be applied to obtain breathing rate if normal breathing class is detected or alarms may be generated if stop breathing is detected, and fine-grained classification of activities may be pursued if the erratic signal is detected. Experiments were performed using a single-channel pulse-modulated continuous wave radar with three subjects for a total of 135 min. In each experiment, the subject was continuously monitored for 15 min and the subject performed activities that resulted in a signal that belonged to one of the three classes. Besides extracting a feature that assessed the distribution of energy of the signal in the frequency domain, a novel nonlinear time series feature extraction method based on the higher-dimensional embedding technique was applied to ascertain periodicity of the reflected signal. Bayes classifier was used to classify each 5-s segment of radar returns. A 30-fold cross validation resulted in 97% of overall classification accuracy.

http://iet.metastore.ingenta.com/content/journals/10.1049/iet-spr.2018.5245
Loading

Related content

content/journals/10.1049/iet-spr.2018.5245
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address