Comparison of Center Estimation Algorithms for Heart and Respiration Monitoring With Microwave Doppler Radar

Zakrzewski, Mari; Raittinen, Harri; Vanhala, Jukka

doi:10.1109/jsen.2011.2119299

Cited by 105 publications

(66 citation statements)

References 13 publications

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“…A received CW signal contains not only the echo from the subject but also echoes from other objects called clutters and crosstalk between antennas. These undesirable components must be removed using DC suppression techniques [1,8] and the center estimation method [10,19]. Such techniques, however, work only when the clutter is stationary; if there is more than one person or other moving objects in the scene, their motion cannot be distinguished from that of the target.…”

Section: Introductionmentioning

confidence: 99%

Accurate heartbeat monitoring using ultra-wideband radar

Sakamoto

Imasaka

Taki

et al. 2015

IEICE Electron. Express

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Abstract:We demonstrate an accurate remote measurement of heartbeats using an ultra-wideband radar system. Although most conventional systems use either continuous waves or impulse-radio systems for remote vital monitoring, continuous waves suffer from non-stationary clutters, while impulse-radio systems cannot detect heartbeats. Our ultra-wideband radar system has a moderate fractional bandwidth intermediate of these systems, resulting in both the suppression of clutters and high sensitivity in measuring accurate heart rates even in a dynamic environment. A simultaneous measurement of the vital signal of a participant employing the ultra-wideband radar and electrocardiography reveals the high accuracy of the radar system in measuring the heart rate varying over time. Keywords: ultra-wideband, radar, vital monitoring, heart rate Classification: Microwave and millimeter wave devices, circuits, and systems References

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Section: Introductionmentioning

confidence: 99%

Accurate heartbeat monitoring using ultra-wideband radar

Sakamoto

Imasaka

Taki

et al. 2015

IEICE Electron. Express

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“…It has been increasingly used for sleep stage classification in recent years [5], [6], [7] as long as it can be unobtrusively obtained with e.g., textile bed sensors [8], Doppler radar [9], and camera [10]. For that purpose, many respiratory features have been investigated.…”

Section: Introductionmentioning

confidence: 99%

Improving sleep/wake detection via boundary adaptation for respiratory spectral features

Long

Haakma

Rolink

et al. 2015

2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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Citation for published version (APA):Long, X., Haakma, R., Rolink, J., Fonseca, P., & Aarts, R. M. (2015). Improving sleep/wake detection via boundary adaptation for respiratory spectral features. In Proceedings of the 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC'15), 25-29 August 2015, Milan, Italy (pp. 374-377 Please check the document version of this publication:• A submitted manuscript is the version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website.• The final author version and the galley proof are versions of the publication after peer review.• The final published version features the final layout of the paper including the volume, issue and page numbers. Link to publicationGeneral rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.• Users may download and print one copy of any publication from the public portal for the purpose of private study or research.• You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal.If the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the "Taverne" license above, please follow below link for the End User Agreement: www.tue.nl/taverne Take down policyIf you believe that this document breaches copyright please contact us at: openaccess@tue.nl providing details and we will investigate your claim. Abstract-In previous work, respiratory spectral features have been successfully used for sleep/wake detection. They are usually extracted from several frequency bands. However, these traditional bands with fixed frequency boundaries might not be the most appropriate to optimize the sleep and wake separation. This is caused by the between-subject variability in physiology, or more specifically, in respiration during sleep. Since the optimal boundaries may relate to mean respiratory frequency over the entire night. Therefore, we propose to adapt these boundaries for each subject in terms of his/her mean respiratory frequency. The adaptive boundaries were considered as those being able to maximize the separation between sleep and wake states by means of their mean power spectral density (PSD) curves overnight. Linear regression models were used to address the association between the adaptive boundaries and mean respiratory frequency based on training data. This was then in turn used to estimate the adaptive boundaries of each test subject. Experiments were c...

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“…As the wavelength decreases, the same amount of motion results in a larger arc [7]. However, that would still not resolve the issue caused by interfering motion from other sources.…”

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confidence: 98%

Small-scale displacement measurement with passive harmonic RF tag using Doppler radar

Gao

Singh

Borić–Lubecke

et al. 2013

2013 IEEE International Wireless Symposium (IWS)

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One way to obtain a greater estimation resolution is to increase the operating frequency of the radar. As the wavelength decreases, the same amount of motion results in a larger arc [7]. However, that would still not resolve the issue caused by interfering motion from other sources.Abstract -Measurement accuracy of the displacement from human cardiopulmonary activities has potential in predicting physiological information for the purpose of biomedical remote sensing and non-contact monitoring. In this paper, an RF frequency doubling tag was used for detecting small displacement with high accuracy. A heterodyne and a homodyne quadrature radar receiving systems were used to detect small scale tag motion and nontagged motion from a distance, respectively. Arctangent demodulation algorithm and imbalance compensation technique were applied to the raw radar data for displacement estimation. Simulation and experimental results were compared, indicating that by using the frequency doubling tag, a better accuracy can be achieved for small displacement estimation with Doppler radar.Doppler radar with frequency doubling tags (harmonic Doppler radar) has been proven to effectively reject any motion arising from non-tagged motion [8]. In such radar system, a passive tag is attached to the subject whose physiological parameters are measured by designing the receiver to receive only the second harmonic generated from the tag. Since the operating frequency of the receiver is doubled, the arc length formed by in-phase and quadrature baseband signals for the same amount of motion is doubled. The increased arc length should result in enhancing the accuracy of center estimation and displacement estimation eventually. This paper presents the results of displacement estimation through the application of passive harmonic tags with Doppler radar systems.

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Comparison of Center Estimation Algorithms for Heart and Respiration Monitoring With Microwave Doppler Radar

Cited by 105 publications

References 13 publications

Accurate heartbeat monitoring using ultra-wideband radar

Accurate heartbeat monitoring using ultra-wideband radar

Improving sleep/wake detection via boundary adaptation for respiratory spectral features

Small-scale displacement measurement with passive harmonic RF tag using Doppler radar

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