scholarly journals Parametric study of antennas for long range Doppler radar heart rate detection

2012 ◽  
Author(s):  
M. Baboli ◽  
A. Singh ◽  
N. Hafner ◽  
V. Lubecke
Keyword(s):  
Heart Rate ◽  
Long Range ◽  
Parametric Study ◽  
Doppler Radar

scholarly journals High Accuracy Heartbeat Detection from CW-Doppler Radar Using Singular Value Decomposition and Matched Filter

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3588
Author(s):  
Yuki Iwata ◽  
Han Trong Thanh ◽  
Guanghao Sun ◽  
Koichiro Ishibashi
Keyword(s):  
Heart Rate ◽  
Singular Value Decomposition ◽  
Continuous Wave ◽  
Doppler Radar ◽  
Matched Filter ◽  
Singular Value ◽  
Home Healthcare ◽  
Body Motion ◽  
Sitting Posture ◽  
Value Decomposition

Heart rate measurement using a continuous wave Doppler radar sensor (CW-DRS) has been applied to cases where non-contact detection is required, such as the monitoring of vital signs in home healthcare. However, as a CW-DRS measures the speed of movement of the chest surface, which comprises cardiac and respiratory signals by body motion, extracting cardiac information from the superimposed signal is difficult. Therefore, it is challenging to extract cardiac information from superimposed signals. Herein, we propose a novel method based on a matched filter to solve this problem. The method comprises two processes: adaptive generation of a template via singular value decomposition of a trajectory matrix formed from the measurement signals, and reconstruction by convolution of the generated template and measurement signals. The method is validated using a dataset obtained in two different experiments, i.e., experiments involving supine and seated subject postures. Absolute errors in heart rate and standard deviation of heartbeat interval with references were calculated as 1.93±1.76bpm and 57.0±28.1s for the lying posture, and 9.72±7.86bpm and 81.3±24.3s for the sitting posture.

Data processing techniques of hydraulic bed sensor and doppler radar for elder care

2017 ◽  
Author(s):  
◽  
Bo-Yu Su
Keyword(s):  
Heart Rate ◽  
Doppler Radar ◽  
Detection Method ◽  
Blood Pressure Measurement ◽  
Fall Detection ◽  
The United States ◽  
Heart Beat ◽  
Body Motion ◽  
Sensor Data ◽  
Heart Rate Monitoring

[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] Population aging is a common phenomenon in a society. The developed country like the United States, eldercare is becoming an important issue nowadays. There are many aspects we need to address for eldercare, including - circulatory system, alimentary system, nervous system and so on. In this research study, we focus on the heart rate monitoring and estimation using a hydraulic bed sensor. In addition, we also develop the fall detection technique using a Doppler radar. The hydraulic bed sensor for heart rate monitoring is placed under the mattress. The sensor system contains four tubes filled with water and uses the pressure sensor to obtain the Ballistocardiogram (BCG) signal. The BCG signal contains the information of heart beat, respiratory rate and body motion. Two algorithms are developed to process the bed sensor data. One uses the Hilbert transform and the other is based on the energy. By using the algorithms we developed, we can extract the heart beat information to estimate the heart rate. The system has been validated in a well controlled lab environment and a nursing house. In addition to the heart rate, the relative blood pressure measurement by using two features extracted from the bed sensor signal has also been developed and validated with 48 people data. The results show high correlation coefficient with the groundtruth. The Doppler radar for human fall detection is mounted in the ceiling. The radar senses the motion of an object and produces outputs based on the Doppler shift effect. We propose an effective method based on Wavelet Transform (WT) for fall vs. nonfall classification. The proposed fall detection classi er can distinguish between the fall and daily activities. The good performance of the proposed detection method has been validated through the data from the lab and in-home environments, with the falls from stunt actors and senior residents. To further improve the performance, we introduce an additional radar mounted on the wall. Based on the same detection method as when using one radar, we extract and concatenate the features from two radars for classification. The result shows outstanding improvement.

Polyphase-Basis Discrete Cosine Transform for Real-Time Measurement of Heart Rate With CW Doppler Radar

2018 ◽  
Vol 66 (3) ◽  
pp. 1644-1659 ◽  
Author(s):  
Junhyeong Park ◽  
Jong-Wook Ham ◽  
Seungwoon Park ◽  
Do-Hoon Kim ◽  
Seong-Jin Park ◽  
...  
Keyword(s):  
Heart Rate ◽  
Real Time ◽  
Discrete Cosine Transform ◽  
Doppler Radar ◽  
Time Measurement ◽  
Cosine Transform ◽  
Real Time Measurement
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