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Radar Sensing for Human Healthcare

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Radar Sensors".

Deadline for manuscript submissions: closed (30 April 2023) | Viewed by 2809

Special Issue Editor


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Guest Editor
School of Artificial Intelligence, Beijing University of Posts and Telecommunications, 10 Xitucheng Road, Haidian District, Beijing 100876, China
Interests: ultra-wideband bio-radar imaging and vital signal detection; network information processing and Internet of Vehicles
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, radar-based human healthcare sensing has proven an efficient method for vital signs acquisition and anomaly detection in many modern applications. Radar Sensing for Human Healthcare is a very promising methodology that causes less discomfort compared with traditional contact-based medical devices and more intelligent healthcare systems. In smart home application, sleep monitoring based on radar has attracted particular interest due to the high demand for non-invasive sleep-monitoring solutions and the surging number of sleep-related disorders. Robust and accurate fall detection is essential to provide immediate medical care and to reduce the severe post-fall consequences. All of these radar-sensing-based human healthcare applications are dependent on advanced signal-processing methods and recently developed deep learning algorithms.

We would like to invite you to contribute to the field of Radar Sensing for Human Healthcare by submitting articles discussing your recent research. Contributions may discuss (but are not limited to) the following topics:

  1. Vital signs monitoring based on radar;
  2. Heart rate variability and respiratory abnormalities estimation based on radar;
  3. Human activity estimation and fall detection warning based on radar;
  4. Sleep monitoring and sleep disorder detection based on radar;
  5. People detection and localization based on radar networking

Prof. Dr. Lin Zhang
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (1 paper)

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Research

10 pages, 1852 KiB  
Article
Estimation of Urine Flow Velocity Using Millimeter-Wave FMCW Radar
by Yingnan Qi, Hyounjoong Kong and Youngwook Kim
Sensors 2022, 22(23), 9402; https://doi.org/10.3390/s22239402 - 2 Dec 2022
Cited by 3 | Viewed by 2111
Abstract
This study investigated the feasibility of remotely estimating the urinary flow velocity of a human subject with high accuracy using millimeter-wave radar. Uroflowmetry is a measurement that involves the speed and volume of voided urine to diagnose benign prostatic hyperplasia or bladder abnormalities. [...] Read more.
This study investigated the feasibility of remotely estimating the urinary flow velocity of a human subject with high accuracy using millimeter-wave radar. Uroflowmetry is a measurement that involves the speed and volume of voided urine to diagnose benign prostatic hyperplasia or bladder abnormalities. Traditionally, the urine velocity during urination has been determined indirectly by analyzing the urine weight during urination. The maximum velocity and urination pattern were then used as a reference to determine the health condition of the prostate and bladder. The traditional uroflowmetry comprises an indirect measurement related to the flow path to the reservoir that causes time delay and water waves that impact the weight. We proposed radar-based uroflowmetry to directly measure the velocity of urine flow, which is more accurate. We exploited Frequency-Modulated Continuous-Wave (FMCW) radar that provides a range-Doppler diagram, allowing extraction of the velocity of a target at a certain range. To verify the proposed method, first, we measured water speed from a water hose using radar and compared it to a calculated value. Next, to emulate the urination scenario, we used a squeezable dummy bladder to create a streamlined water flow in front of the millimeter-wave FMCW radar. We validated the result by concurrently employing the traditional uroflowmetry that is based on a weight sensor to compare the results with the proposed radar-based method. The comparison of the two results confirmed that radar velocity estimation can yield results, confirmed by the traditional method, while demonstrating more detailed features of urination. Full article
(This article belongs to the Special Issue Radar Sensing for Human Healthcare)
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