Modern Forms and New Challenges in Medical Sensors and Body Area Networks
1
School of Computing and Mathematical Sciences, University of Leicester, Leicester LE1 7RH, UK
2
Department of Signal Theory, Telematics and Communications, University of Granada, 18071 Granada, Spain
*
Author to whom correspondence should be addressed.
J. Sens. Actuator Netw. 2022, 11(4), 79; https://doi.org/10.3390/jsan11040079
Submission received: 10 November 2022
/
Accepted: 22 November 2022
/
Published: 23 November 2022
(This article belongs to the Section Actuators, Sensors and Devices)
Traditional medical sensors/monitors can measure pressure, airflow, force, oxygen, pulse, temperature, etc. They can continuously measure vital signals and/or repeatedly perform medical tests [1]; help in monitoring for cardiac events [2], hemodynamics, respiration [3], glucose levels, and body temperature; and benefit individuals in long-term care [4] and those with automated insulin pumps [5]. However, a shortcoming is the immobility of some of the large, heavy sensors.
Hence, the patient requirement for portability has popularized the use of portable sensors. Body area network (BAN) technologies utilize low-power wireless gadgets, which are either embedded in or carried on the body [6]. Another advantage of BANs is the location-independent monitoring facility [7]. However, a shortcoming is that they do not offer any view of the inside of the body, meaning that they may not help doctors in situations that require internal examination.
On the other hand, medical imaging sensors—for example, X-ray, magnetic resonance imaging (MRI), computed tomography (CT), positron emission tomography (PET), and single-photon emission computed tomography (SPECT)—play an essential role in checking the anatomic structure, functional, and physiological processes of the body. Using medical imaging sensors, doctors can precisely make a diagnosis and recommend complementary treatments for many diseases, such as Alzheimer’s disease [8], brain tumors [9], and other diseases [10], such as alcoholism, COVID-19 [11], etc.
We believe that medical sensors and body area networks complement each other, with the ultimate goal of providing better services for patients and doctors. All related papers with the potential to improve methods in their fields or those that report on recent advances are welcome in the current Special Issue: “Medical Sensors and Body Area Networks”.
Funding
This research received no external funding.
Conflicts of Interest
The authors declare that the research was conducted without any commercial or financial relationships that could be construed as potential conflicts of interest.
References
- Halley, S.; Ramaiyan, K.P.; Tsui, L.K.; Garzon, F. A review of zirconia oxygen, NOx, and mixed potential gas sensors—History and current trends. Sens. Actuators B Chem. 2022, 370, 132363. [Google Scholar] [CrossRef]
- Flick, M.; Jobeir, A.; Hoppe, P.; Kubik, M.; Rogge, D.E.; Schulte-Uentrop, L.; Kouz, K.; Saugel, B. A new noninvasive finger sensor (NICCI system) for cardiac output monitoring A method comparison study in patients after cardiac surgery. Eur. J. Anaesthesiol. 2022, 39, 695–700. [Google Scholar] [CrossRef] [PubMed]
- Zaltieri, M.; Massaroni, C.; Di Tocco, J.; Bravi, M.; Morrone, M.; Sterzi, S.; Caponero, M.A.; Schena, E.; Lo Presti, D. Preliminary assessment of a flexible multi-sensor wearable system based on fiber bragg gratings for respiratory monitoring of hemiplegic patients. Int. J. Environ. Res. Public Health 2022, 19, 13525. [Google Scholar] [CrossRef] [PubMed]
- Joo, J.E.; Hu, Y.; Kim, S.; Kim, H.; Park, S.; Kim, J.H.; Kim, Y.; Park, S.M. An indoor-monitoring LiDAR sensor for patients with alzheimer disease residing in long-term care facilities. Sensors 2022, 22, 7934. [Google Scholar] [CrossRef] [PubMed]
- Burnside, M.J.; Lewis, D.M.; Crocket, H.; Meier, R.; Williman, J.; Sanders, O.J.; Jefferies, C.A.; Faherty, A.M.; Paul, R.; Lever, C.S.; et al. The CREATE trial: Randomized clinical trial comparing open-source automated insulin delivery with sensor augmented pump therapy in type 1 diabetes. Diabetes 2022, 71, 286. [Google Scholar] [CrossRef]
- Nusrat, T.; Dawod, F.S.; Islam, T.; Kunkolienker, P.; Roy, S.; Rahman, M.M.; Ghosh, S.; Dey, S.; Mitra, D.; Braaten, B.D. A comprehensive study on next-generation electromagnetics devices and techniques for internet of everything (IoE). Electronics 2022, 11, 3341. [Google Scholar] [CrossRef]
- Sid, A.; Cresson, P.Y.; Joly, N.; Braud, F.; Lasri, T. A flexible and wearable dual band bio-based antenna for WBAN applications. AEU-Int. J. Electron. Commun. 2022, 157, 154412. [Google Scholar] [CrossRef]
- Ullah, Z.; Jamjoom, M. A deep learning for alzheimer’s stages detection using brain images. CMC-Comput. Mat. Contin. 2022, 74, 1457–1473, 032752. [Google Scholar] [CrossRef]
- Rathore, F.A.; Khan, H.S.; Ali, H.M.; Obayya, M.; Rasheed, S.; Hussain, L.; Kazmi, Z.H.; Nour, M.K.; Mohamed, A.; Motwakel, A. Survival prediction of glioma patients from integrated radiology and pathology images using machine learning ensemble regression methods. Appl. Sci. 2022, 12, 10357. [Google Scholar] [CrossRef]
- Zhu, Z.; Lu, S.; Wang, S.-H.; Gorriz, J.M.; Zhang, Y.-D. DSNN: A DenseNet-based SNN for explainable brain disease classification. Front. Syst. Neurosci. 2022, 16, 838822. [Google Scholar] [CrossRef] [PubMed]
- Wang, S.-H. COVID-19 classification by FGCNet with deep feature fusion from graph convolutional network and convolutional neural network. Inf. Fusion 2021, 67, 208–229. [Google Scholar] [CrossRef] [PubMed]
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
MDPI and ACS Style
Zhang, Y.; Gorriz, J.M.; Wang, S. Modern Forms and New Challenges in Medical Sensors and Body Area Networks. J. Sens. Actuator Netw. 2022, 11, 79. https://doi.org/10.3390/jsan11040079
AMA Style
Zhang Y, Gorriz JM, Wang S. Modern Forms and New Challenges in Medical Sensors and Body Area Networks. Journal of Sensor and Actuator Networks. 2022; 11(4):79. https://doi.org/10.3390/jsan11040079
Chicago/Turabian StyleZhang, Yudong, Juan Manuel Gorriz, and Shuihua Wang. 2022. "Modern Forms and New Challenges in Medical Sensors and Body Area Networks" Journal of Sensor and Actuator Networks 11, no. 4: 79. https://doi.org/10.3390/jsan11040079
APA StyleZhang, Y., Gorriz, J. M., & Wang, S. (2022). Modern Forms and New Challenges in Medical Sensors and Body Area Networks. Journal of Sensor and Actuator Networks, 11(4), 79. https://doi.org/10.3390/jsan11040079
Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.
