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Electromagnetic Sensors for Remote Patient Monitoring
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Electromagnetic Sensors for Remote Patient Monitoring

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

Deadline for manuscript submissions: 31 October 2024 | Viewed by 3232

Special Issue Editors

Dr. Marco Mercuri

E-Mail Website
Guest Editor
Dipartimento di Informatica, Modellistica, Elettronica e Sistemistica (DIMES), University of Calabria, 87036 Rende, CS, Italy
Interests: microwave/RF radar systems and algorithms; remote radar sensing; contactless health monitoring; biomedical applications; wireless sensor networks
Prof. Dr. Changzhi Li

E-Mail Website
Guest Editor
Department of Electrical & Computer Engineering, Texas Tech University, Box 43102, Lubbock, TX 79409-3102, USA
Interests: radio frequency and microwave; wireless localization; non-contact motion sensing; healthcare monitoring; structural monitoring; biomedical radar
Special Issues, Collections and Topics in MDPI journals
Dr. Francesco Fioranelli

E-Mail Website
Guest Editor
MS3-Microwave Sensing Signals and Systems, TU Delft, 2628CD Delft, The Netherlands
Interests: radar systems and radar signal processing; micro-Doppler signatures

Special Issue Information

Dear Colleagues,

The continuous and rapid growth of the combined senior and geriatric population has resulted in an increase of age-related chronic diseases. This situation results in a shortage of healthcare personnel, together with the ever-increasing demands for healthcare services. Coupled with the expected rise in healthcare cost and since only a minority could afford private home-care personnel, the need for technologies enabling remote patient monitoring is certainly on the rise. In this respect, in the last two decades, radar and Wi-Fi technologies have been significantly attracting the attention of many researchers worldwide. The capability of measuring movements, speed and distance of multiple subjects, even though clothes, blankets, and many barriers (e.g. glass, doors, walls, etc.), in various ambient light and weather conditions, allows the use of these technologies for vital signs monitoring, fall detection and human tracking. Moreover, the monitoring can be performed continuously while preserving the privacy of the subjects and without adverse health effects. This opens a multitude of healthcare and ambient assisted living applications.

In this Special Issue, we welcome research articles describing the most recent advances and innovations in the hardware, software, signal processing algorithms, and applications of radar and Wi-Fi sensors for remote patient monitoring.

Dr. Marco Mercuri
Prof. Dr. Changzhi Li
Dr. Francesco Fioranelli
Guest Editors

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 (3 papers)

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Research

18 pages, 1647 KiB  
Article
Respiration and Heart Rate Monitoring in Smart Homes: An Angular-Free Approach with an FMCW Radar
by Pouya Mehrjouseresht, Reda El Hail, Peter Karsmakers and Dominique M. M.-P. Schreurs
Sensors 2024, 24(8), 2448; https://doi.org/10.3390/s24082448 - 11 Apr 2024
Viewed by 587
Abstract
This paper proposes a new approach for wide angle monitoring of vital signs in smart home applications. The person is tracked using an indoor radar. Upon detecting the person to be static, the radar automatically focuses its beam on that location, and subsequently [...] Read more.
This paper proposes a new approach for wide angle monitoring of vital signs in smart home applications. The person is tracked using an indoor radar. Upon detecting the person to be static, the radar automatically focuses its beam on that location, and subsequently breathing and heart rates are extracted from the reflected signals using continuous wavelet transform (CWT) analysis. In this way, leveraging the radar’s on-chip processor enables real-time monitoring of vital signs across varying angles. In our experiment, we employ a commercial multi-input multi-output (MIMO) millimeter-wave FMCW radar to monitor vital signs within a range of 1.15 to 2.3 m and an angular span of −44.8 to +44.8 deg. In the Bland–Altman plot, the measured results indicate the average difference of −1.5 and 0.06 beats per minute (BPM) relative to the reference for heart rate and breathing rate, respectively. Full article
(This article belongs to the Special Issue Electromagnetic Sensors for Remote Patient Monitoring)
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19 pages, 11670 KiB  
Article
Incorporation of Digital Modulation into Vital Sign Detection and Gesture Recognition Using Multimode Radar Systems
by Michael C. Brown and Changzhi Li
Sensors 2023, 23(18), 7675; https://doi.org/10.3390/s23187675 - 5 Sep 2023
Viewed by 1132
Abstract
The incorporation of digital modulation into radar systems poses various challenges in the field of radar design, but it also offers a potential solution to the shrinking availability of low-noise operating environments as the number of radar applications increases. Additionally, digital systems have [...] Read more.
The incorporation of digital modulation into radar systems poses various challenges in the field of radar design, but it also offers a potential solution to the shrinking availability of low-noise operating environments as the number of radar applications increases. Additionally, digital systems have reached a point where available components and technology can support higher speeds than ever before. These advancements present new avenues for radar design, in which digitally controlled phase-modulated continuous wave (PMCW) radar systems can look to support multiple collocated radar systems with low radar-radar interference. This paper proposes a reconfigurable PMCW radar for use in vital sign detection and gesture recognition while utilizing digital carrier modulation and compares the radar responses of various modulation schemes. Binary sequences are used to introduce phase modulation to the carrier wave by use of a field programable gate array (FPGA), allowing for flexibility in the modulation speed and binary sequence. Experimental results from the radar demonstrate the differences between CW and PMCW modes when measuring the respiration rate of a human subject and in gesture detection. Full article
(This article belongs to the Special Issue Electromagnetic Sensors for Remote Patient Monitoring)
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14 pages, 3150 KiB  
Article
Whitening-Aided Learning from Radar Micro-Doppler Signatures for Human Activity Recognition
by Zahra Sadeghi Adl and Fauzia Ahmad
Sensors 2023, 23(17), 7486; https://doi.org/10.3390/s23177486 - 28 Aug 2023
Cited by 1 | Viewed by 884
Abstract
Deep learning architectures are being increasingly adopted for human activity recognition using radar technology. A majority of these architectures are based on convolutional neural networks (CNNs) and accept radar micro-Doppler signatures as input. The state-of-the-art CNN-based models employ batch normalization (BN) to optimize [...] Read more.
Deep learning architectures are being increasingly adopted for human activity recognition using radar technology. A majority of these architectures are based on convolutional neural networks (CNNs) and accept radar micro-Doppler signatures as input. The state-of-the-art CNN-based models employ batch normalization (BN) to optimize network training and improve generalization. In this paper, we present whitening-aided CNN models for classifying human activities with radar sensors. We replace BN layers in a CNN model with whitening layers, which is shown to improve the model’s accuracy by not only centering and scaling activations, similar to BN, but also decorrelating them. We also exploit the rotational freedom afforded by whitening matrices to align the whitened activations in the latent space with the corresponding activity classes. Using real data measurements of six different activities, we show that whitening provides superior performance over BN in terms of classification accuracy for a CNN-based classifier. This demonstrates the potential of whitening-aided CNN models to provide enhanced human activity recognition with radar sensors. Full article
(This article belongs to the Special Issue Electromagnetic Sensors for Remote Patient Monitoring)
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