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Radar Sensing for Health Monitoring
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Radar Sensing for Health Monitoring

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

Deadline for manuscript submissions: closed (25 August 2023) | Viewed by 5375

Special Issue Editors

Dr. Ennio Gambi

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Guest Editor
Department of Information Engineering, Università Politecnica delle Marche，Via Brecce Bianche 12, 60131 Ancona, Italy
Interests: terahertz radar; FMCW radar; vital signs monitoring
Special Issues, Collections and Topics in MDPI journals
Dr. Linda Senigagliesi

E-Mail
Guest Editor
Department of Information Engineering, Università Politecnica delle Marche, Via Brecce Bianche 12, 60131 Ancona, Italy
Interests: communication systems; machine learning techniques for radar applications; physical layer security
Special Issues, Collections and Topics in MDPI journals
Dr. Gianluca Ciattaglia

E-Mail Website
Guest Editor
Department of Information Engineering, Università Politecnica delle Marche, Via Brecce Bianche 12, 60131 Ancona, Italy
Interests: automotive radars; radar measurements; radar digital signal processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

There has been a great deal of research on the use of sensors to monitor people's health, with the aim of improving their quality of life. It is evident that the context of subject monitoring comprises a wide range of applications, involving the use of extremely differentiated sensor technologies capable of monitoring many aspects of people's health. The monitoring of human behavior is always an interesting topic in radar research, and many radar technologies have been applied to extract human-related information from signals. Since the recognition of human health characteristics can be crucial for improving and preserving people's lives, the progress of radar technology in this field is extremely exciting. Improvements in these technologies in recent years open up a wide range of possibilities, with sensors now available at different radio carrier frequencies and with very wide bands, making it possible to explore fields that were previously unattainable due to hardware limitations. Toward this end, signal processing approaches such as those based on micro-Doppler could be beneficial. In fact, they have recently been applied in the field of human health and behavior recognition, often in association with modern machine learning techniques.

This Special Issue intends to collect contributions of the most recent research activity in the context of people health monitoring based on radar technology.

Prof. Dr. Ennio Gambi
Dr. Linda Senigagliesi
Dr. Gianluca Ciattaglia
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.

Keywords

  • micro-Doppler radar
  • FMCW radar
  • UWB radar
  • automotive radar
  • MIMO radar
  • machine learning
  • radar signal processing
  • vital parameters

Published Papers (3 papers)

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Research

22 pages, 30535 KiB  
Article
Enhancing Diagnosis of Anterior and Inferior Myocardial Infarctions Using UWB Radar and AI-Driven Feature Fusion Approach
by Kainat Zafar, Hafeez Ur Rehman Siddiqui, Abdul Majid, Furqan Rustam, Sultan Alfarhood, Mejdl Safran and Imran Ashraf
Sensors 2023, 23(18), 7756; https://doi.org/10.3390/s23187756 - 08 Sep 2023
Viewed by 908
Abstract
Despite significant improvement in prognosis, myocardial infarction (MI) remains a major cause of morbidity and mortality around the globe. MI is a life-threatening cardiovascular condition that requires prompt diagnosis and appropriate treatment. The primary objective of this research is to identify instances of [...] Read more.
Despite significant improvement in prognosis, myocardial infarction (MI) remains a major cause of morbidity and mortality around the globe. MI is a life-threatening cardiovascular condition that requires prompt diagnosis and appropriate treatment. The primary objective of this research is to identify instances of anterior and inferior myocardial infarction by utilizing data obtained from Ultra-wideband radar technology in a hospital for patients of anterior and inferior MI. The collected data is preprocessed to extract spectral features. A novel feature engineering approach is designed to fuse temporal features and class prediction probability features derived from the spectral feature dataset. Several well-known machine learning models are implemented and fine-tuned to obtain optimal performance in the detection of anterior and inferior MI. The results demonstrate that integration of the fused feature set with machine learning models results in a notable improvement in both the accuracy and precision of MI detection. Notably, random forest (RF) and k-nearest neighbor showed superb performance with an accuracy of 98.8%. For demonstrating the capacity of models to generalize, K-fold cross-validation is carried out, wherein RF exhibits a mean accuracy of 99.1%. Furthermore, the examination of computational complexity indicates a low computational complexity, thereby indicating computational efficiency. Full article
(This article belongs to the Special Issue Radar Sensing for Health Monitoring)
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24 pages, 9176 KiB  
Article
Evaluation Protocol for Analogue Intelligent Medical Radars: Towards a Systematic Approach Based on Theory and a State of the Art
by Geoffray Battiston, Rémi Régnier and Olivier Galibert
Sensors 2023, 23(6), 3036; https://doi.org/10.3390/s23063036 - 11 Mar 2023
Viewed by 1263
Abstract
We propose the basis for a systematised approach to the performance evaluation of analogue intelligent medical radars. In the first part, we review the literature on the evaluation of medical radars and compare the provided experimental elements with models from radar theory in [...] Read more.
We propose the basis for a systematised approach to the performance evaluation of analogue intelligent medical radars. In the first part, we review the literature on the evaluation of medical radars and compare the provided experimental elements with models from radar theory in order to identify the key physical parameters that will be useful to develop a comprehensive protocol. In the second part, we present our experimental equipment, protocol and metrics to carry out such an evaluation. Full article
(This article belongs to the Special Issue Radar Sensing for Health Monitoring)
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14 pages, 6365 KiB  
Article
Intelligent Reflecting Surface-Based Non-LOS Human Activity Recognition for Next-Generation 6G-Enabled Healthcare System
by Umer Saeed, Syed Aziz Shah, Muhammad Zakir Khan, Abdullah Alhumaidi Alotaibi, Turke Althobaiti, Naeem Ramzan and Qammer H. Abbasi
Sensors 2022, 22(19), 7175; https://doi.org/10.3390/s22197175 - 21 Sep 2022
Cited by 6 | Viewed by 2062
Abstract
Human activity monitoring is a fascinating area of research to support autonomous living in the aged and disabled community. Cameras, sensors, wearables, and non-contact microwave sensing have all been suggested in the past as methods for identifying distinct human activities. Microwave sensing is [...] Read more.
Human activity monitoring is a fascinating area of research to support autonomous living in the aged and disabled community. Cameras, sensors, wearables, and non-contact microwave sensing have all been suggested in the past as methods for identifying distinct human activities. Microwave sensing is an approach that has lately attracted much interest since it has the potential to address privacy problems caused by cameras and discomfort caused by wearables, especially in the healthcare domain. A fundamental drawback of the current microwave sensing methods such as radar is non-line-of-sight and multi-floor environments. They need precise and regulated conditions to detect activity with high precision. In this paper, we have utilised the publicly available online database based on the intelligent reflecting surface (IRS) system developed at the Communications, Sensing and Imaging group at the University of Glasgow, UK (references 39 and 40). The IRS system works better in the multi-floor and non-line-of-sight environments. This work for the first time uses algorithms such as support vector machine Bagging and Decision Tree on the publicly available IRS data and achieves better accuracy when a subset of the available data is considered along specific human activities. Additionally, the work also considers the processing time taken by the classier in training stage when exposed to the IRS data which was not previously explored. Full article
(This article belongs to the Special Issue Radar Sensing for Health Monitoring)
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