scholarly journals A Doppler radar vital sign detection system using concurrent dual-band hybrid down conversion architecture

2020 ◽  
Vol 17 (1) ◽  
pp. 20190665-20190665
Author(s):  
Wen-Kui Liu ◽  
Hai-Peng Fu ◽  
Zi-Kai Yang
2010 ◽  
Vol 59 (6) ◽  
pp. 1580-1588 ◽  
Author(s):  
Changzhan Gu ◽  
Changzhi Li ◽  
Jenshan Lin ◽  
Jiang Long ◽  
Jiangtao Huangfu ◽  
...  

2013 ◽  
Vol 56 (2) ◽  
pp. 391-394 ◽  
Author(s):  
Brijesh Iyer ◽  
Nagendra P. Pathak

Sensors ◽  
2020 ◽  
Vol 20 (15) ◽  
pp. 4183 ◽  
Author(s):  
Zi-Kai Yang ◽  
Heping Shi ◽  
Sheng Zhao ◽  
Xiang-Dong Huang

The non-contact detection of human vital signs (i.e., respiration rate (RR) and heartbeat rate (HR)) using a continuous-wave (CW) Doppler radar sensor has great potential for intensive care monitoring, home healthcare, etc. However, large-scale and fast random body movement (RBM) has been a bottleneck for vital sign detection using a single CW Doppler radar. To break this dilemma, this study proposed a scheme combining adaptive noise cancellation (ANC) with polynomial fitting, which could retrieve the weak components of both respiration and heartbeat signals that were submerged under serious RBM interference. In addition, the new-type discrete cosine transform (N-DCT) was introduced to improve the detection accuracy. This scheme was first verified using a numerical simulation. Then, experiments utilizing a 10-GHz Doppler radar sensor that was built from general-purpose radio frequency (RF) and communication instruments were also carried out. No extra RF/microwave components and modules were needed, and neither was a printed circuit board nor an integrated-chip design required. The experimental results showed that both the RR and HR could still be extracted during large-scale and fast body movements using only a single Doppler radar sensor because the RBM noises could be greatly eliminated by utilizing the proposed ANC algorithm.


Author(s):  
Ping-Hsun Wu ◽  
Je-Kuan Jau ◽  
Chien-Jung Li ◽  
Tzyy-Sheng Horng ◽  
Powen Hsu

Author(s):  
Takenori Obo ◽  
Toshiyuki Sawayama ◽  
Takuya Sawayama ◽  
Naoyuki Kubota

Sensors ◽  
2018 ◽  
Vol 18 (3) ◽  
pp. 694 ◽  
Author(s):  
Nguyen Phuoc Van ◽  
Liqiong Tang ◽  
Subhas Mukhopadhyay ◽  
Duc Nguyen ◽  
Faraz Hasan

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