Respiration-rate estimation of a moving target using impulse-based ultra wideband radars

2011 ◽  
Vol 35 (1) ◽  
pp. 31-39 ◽  
Author(s):  
Azadeh Sharafi ◽  
Mehran Baboli ◽  
Mohammad Eshghi ◽  
Alireza Ahmadian
Keyword(s):  
Respiration Rate ◽  
Ultra Wideband ◽  
Moving Target ◽  
Rate Estimation

Improved human respiration detection method via ultra‐wideband radar in through‐wall or other similar conditions

2016 ◽  
Vol 10 (3) ◽  
pp. 468-476 ◽  
Author(s):  
Shiyou Wu ◽  
Kai Tan ◽  
Zhenghuan Xia ◽  
Jie Chen ◽  
Shengwei Meng ◽  
...  
Keyword(s):  
Detection Method ◽  
Ultra Wideband ◽  
Human Respiration ◽  
Wideband Radar

scholarly journals Person-Specific Heart Rate Estimation With Ultra-Wideband Radar Using Convolutional Neural Networks

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 168484-168494 ◽  
Author(s):  
Shuqiong Wu ◽  
Takuya Sakamoto ◽  
Kentaro Oishi ◽  
Toru Sato ◽  
Kenichi Inoue ◽  
...  
Keyword(s):  
Neural Networks ◽  
Heart Rate ◽  
Convolutional Neural Networks ◽  
Ultra Wideband ◽  
Rate Estimation ◽  
Wideband Radar ◽  
Heart Rate Estimation

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

Sensors ◽  
2019 ◽  
Vol 19 (15) ◽  
pp. 3340 ◽  
Author(s):  
Seong-Hoon Kim ◽  
Zong Woo Geem ◽  
Gi-Tae Han
Keyword(s):  
Recognition Rate ◽  
The Body ◽  
Physical Contact ◽  
Ultra Wideband ◽  
Breathing Patterns ◽  
Sleep Analysis ◽  
Average Recognition Rate ◽  
Human Respiration ◽  
Rate Experiment ◽  
Wideband Radar

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).

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