A Novel Human Respiration Pattern Recognition Using Signals of Ultra-Wideband Radar Sensor

Recently, various studies have been conducted on the quality of sleep in medical and health care fields. Sleep analysis in these areas is typically performed through polysomnography. However, since polysomnography involves attaching sensor devices to the body, accurate sleep measurements may be diff...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2019-07, Vol.19 (15), p.3340
Main Authors: Kim, Seong-Hoon, Geem, Zong Woo, Han, Gi-Tae
Format: Article
Language:English
Subjects:
Algorithms
Apnea - physiopathology
Artificial intelligence
CNN
Data acquisition
Humans
International conferences
Machine learning
Methods
Motion
Neural networks
Neural Networks, Computer
Pattern recognition
Polysomnography - methods
Quality
Radar
Radar systems
Respiration
respiration pattern
Sensors
Signal processing
Signal Processing, Computer-Assisted
Sleep apnea
sleep quality
Tachypnea
Tachypnea - physiopathology
ultra-wideband radar
Ultrawideband radar
Wireless communications
Wireless networks
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Summary:Recently, various studies have been conducted on the quality of sleep in medical and health care fields. Sleep analysis in these areas is typically performed through polysomnography. However, since polysomnography involves attaching sensor devices to the body, accurate sleep measurements may be difficult due to the inconvenience and sensitivity of physical contact. In recent years, research has been focused on using sensors such as Ultra-wideband Radar, which can acquire bio-signals even in a non-contact environment, to solve these problems. In this paper, we have acquired respiratory signal data using Ultra-wideband Radar and proposed 1D CNN (1-Dimension Convolutional Neural Network) model that can classify and recognize five respiration patterns (Eupnea, Bradypnea, Tachypnea, Apnea, and Motion) from the signal data. Also, in the proposed model, we find the optimum parameter range through the recognition rate experiment on the combination of parameters (layer depth, size of kernel, and number of kernels). The average recognition rate of five breathing patterns experimented by applying the proposed method was 93.9%, which is about 3%~13% higher than that of conventional methods (LDA, SVM, and MLP).
ISSN:1424-8220
1424-8220
DOI:10.3390/s19153340