High Accuracy Heartbeat Detection from CW-Doppler Radar Using Singular Value Decomposition and Matched Filter
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...
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| Published in | Sensors (Basel, Switzerland) Vol. 21; no. 11; p. 3588 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
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21.05.2021
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| Abstract | 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. |
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| AbstractList | 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. 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.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. 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 ± 1.76 bpm and 57.0 ± 28.1 s for the lying posture, and 9.72 ± 7.86 bpm and 81.3 ± 24.3 s for the sitting posture. 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±1.76 bpm and 57.0±28.1 s for the lying posture, and 9.72±7.86 bpm and 81.3±24.3 s for the sitting posture. |
| Author | Iwata, Yuki Ishibashi, Koichiro Thanh, Han Trong Sun, Guanghao |
| AuthorAffiliation | 2 School of Electronics and Telecommunications, Hanoi University of Science and Technology (HUST), Hanoi 100000, Vietnam; thanh.hantrong@hust.edu.vn 1 Graduate School of Informatics and Engineering, The University of Electro-Communications (UEC), Tokyo 182-8585, Japan; guanghao.sun@uec.ac.jp (G.S.); ishibashi@uec.ac.jp (K.I.) |
| AuthorAffiliation_xml | – name: 1 Graduate School of Informatics and Engineering, The University of Electro-Communications (UEC), Tokyo 182-8585, Japan; guanghao.sun@uec.ac.jp (G.S.); ishibashi@uec.ac.jp (K.I.) – name: 2 School of Electronics and Telecommunications, Hanoi University of Science and Technology (HUST), Hanoi 100000, Vietnam; thanh.hantrong@hust.edu.vn |
| Author_xml | – sequence: 1 givenname: Yuki orcidid: 0000-0002-7702-8138 surname: Iwata fullname: Iwata, Yuki – sequence: 2 givenname: Han Trong surname: Thanh fullname: Thanh, Han Trong – sequence: 3 givenname: Guanghao orcidid: 0000-0001-5639-3171 surname: Sun fullname: Sun, Guanghao – sequence: 4 givenname: Koichiro surname: Ishibashi fullname: Ishibashi, Koichiro |
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| SubjectTerms | Accuracy Airports Biometrics continuous wave-Doppler radar Decomposition Doppler radar Heart rate Infectious diseases matched filtering Measurement techniques Noise non-contact vital signs measurement Respiration Signal processing singular value decomposition Time series Vibration |
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| Title | High Accuracy Heartbeat Detection from CW-Doppler Radar Using Singular Value Decomposition and Matched Filter |
| URI | https://www.proquest.com/docview/2539981340 https://www.proquest.com/docview/2536480589 https://pubmed.ncbi.nlm.nih.gov/PMC8196719 https://doaj.org/article/92f6ea7a53244ba4ab18782e41820875 |
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