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Electronics for E-health Sensor Systems
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Electronics for E-health Sensor Systems

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

Deadline for manuscript submissions: closed (31 December 2020) | Viewed by 17376

Special Issue Editors

Prof. Dr. Giorgio Pennazza

E-Mail Website
Guest Editor
Department of Engineering, Campus Bio-Medico University of Rome, 00128 Rome, Italy
Interests: chemical sensors;electronic interface;smart sensors;sensor network
Special Issues, Collections and Topics in MDPI journals
Dr. Alessandro Zompanti

E-Mail Website
Guest Editor
Unit of Electronics for Sensor Systems, Department of Engineering, Campus Bio-Medico, University of Rome, Via Alvaro del Portillo, 21 – 00128 Rome, Italy
Interests: electronic interfaces, electronic systems, sensor systems, sensors arrays, multivariate data analysis

Special Issue Information

Dear Colleagues,

The adoption of E-Health technologies is rapidly increasing: improved computing power and connectivity, software stability, and electronic miniaturization are allowing these technologies to be used to manage health and wellness. Wearable health-monitoring devices, portable medical devices, and point-of-care devices are increasingly helping people to better monitor their health status. These technologies make extensive use of cloud computing and big data to carry out their work. The proper functioning of the whole system mainly depends on the quality of the acquired data. For this reason, particular attention must be paid to designing proper electronic circuits in order achieve the best performance from the employed sensors. The aim of this Special Issue is to explore potential solutions regarding electronic circuits for E-Health applications, and also to present and highlight the advances and the latest novel and emergent results in this field, with particular attention given to innovative sensors and their reliability.

Prof. Dr. Giorgio Pennazza
Dr. Zompanti Alessandro
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

  • sensor interfaces
  • electronics for sensors
  • IoT
  • acquisition systems
  • analog front-end
  • e-health
  • portable medical device
  • wearable
  • point-of-care
  • low power
  • battery-operated device
  • digital diagnostic
  • precision instrumentation

Published Papers (3 papers)

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Research

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12 pages, 1807 KiB  
Article
The Impact of Short-Term Exposure to Air Pollution on the Exhaled Breath of Healthy Adults
by Ariana Lammers, Anne H. Neerincx, Susanne J. H. Vijverberg, Cristina Longo, Nicole A. H. Janssen, A. John F. Boere, Paul Brinkman, Flemming R. Cassee and Anke H. Maitland van der Zee
Sensors 2021, 21(7), 2518; https://doi.org/10.3390/s21072518 - 4 Apr 2021
Cited by 6 | Viewed by 2554
Abstract
Environmental factors, such as air pollution, can affect the composition of exhaled breath, and should be well understood before biomarkers in exhaled breath can be used in clinical practice. Our objective was to investigate whether short-term exposures to air pollution can be detected [...] Read more.
Environmental factors, such as air pollution, can affect the composition of exhaled breath, and should be well understood before biomarkers in exhaled breath can be used in clinical practice. Our objective was to investigate whether short-term exposures to air pollution can be detected in the exhaled breath profile of healthy adults. In this study, 20 healthy young adults were exposed 2–4 times to the ambient air near a major airport and two highways. Before and after each 5 h exposure, exhaled breath was analyzed using an electronic nose (eNose) consisting of seven different cross-reactive metal-oxide sensors. The discrimination between pre and post-exposure was investigated with multilevel partial least square discriminant analysis (PLSDA), followed by linear discriminant and receiver operating characteristic (ROC) analysis, for all data (71 visits), and for a training (51 visits) and validation set (20 visits). Using all eNose measurements and the training set, discrimination between pre and post-exposure resulted in an area under the ROC curve of 0.83 (95% CI = 0.76–0.89) and 0.84 (95% CI = 0.75–0.92), whereas it decreased to 0.66 (95% CI = 0.48–0.84) in the validation set. Short-term exposure to high levels of air pollution potentially influences the exhaled breath profiles of healthy adults, however, the effects may be minimal for regular daily exposures. Full article
(This article belongs to the Special Issue Electronics for E-health Sensor Systems)
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31 pages, 6847 KiB  
Article
Very High Bit Rate Near-Field Communication with Low-Interference Coils and Digital Single-Bit Sampling Transceivers for Biomedical Sensor Systems
by Sebastian Stoecklin, Elias Rosch, Adnan Yousaf and Leonhard Reindl
Sensors 2020, 20(21), 6025; https://doi.org/10.3390/s20216025 - 23 Oct 2020
Cited by 8 | Viewed by 3082
Abstract
The evolution of microelectronics increased the information acquired by today’s biomedical sensor systems to an extent where the capacity of low-power communication interfaces becomes one of the central bottlenecks. Hence, this paper mathematically analyzes and experimentally verifies novel coil and transceiver topologies for [...] Read more.
The evolution of microelectronics increased the information acquired by today’s biomedical sensor systems to an extent where the capacity of low-power communication interfaces becomes one of the central bottlenecks. Hence, this paper mathematically analyzes and experimentally verifies novel coil and transceiver topologies for near-field communication interfaces, which simultaneously allow for high data transfer rates, low power consumption, and reduced interference to nearby wireless power transfer interfaces. Data coil design is focused on presenting two particular topologies which provide sufficient coupling between a reader and a wireless sensor system, but do not couple to an energy coil situated on the same substrate, severely reducing interference between wireless data and energy transfer interfaces. A novel transceiver design combines the approaches of a minimalistic analog front-end with a fully digital single-bit sampling demodulator, in which rectangular binary signals are processed by simple digital circuits instead of sinusoidal signals being conditioned by complex analog mixers and subsequent multi-bit analog-to-digital converters. The concepts are implemented using an analog interface in discrete circuit technology and a commercial low-power field-programmable gate array, yielding a transceiver which supports data rates of up to 6.78 MBit/s with an energy consumption of just 646 pJ/bit in transmitting mode and of 364 pJ/bit in receiving mode at a bit error rate of 2×10−7, being 10 times more energy efficient than any commercial NFC interface and fully implementable without any custom CMOS technology. Full article
(This article belongs to the Special Issue Electronics for E-health Sensor Systems)
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Review

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29 pages, 3124 KiB  
Review
Artificial Intelligence-Based Wearable Robotic Exoskeletons for Upper Limb Rehabilitation: A Review
by Manuel Andrés Vélez-Guerrero, Mauro Callejas-Cuervo and Stefano Mazzoleni
Sensors 2021, 21(6), 2146; https://doi.org/10.3390/s21062146 - 18 Mar 2021
Cited by 51 | Viewed by 11100
Abstract
Processing and control systems based on artificial intelligence (AI) have progressively improved mobile robotic exoskeletons used in upper-limb motor rehabilitation. This systematic review presents the advances and trends of those technologies. A literature search was performed in Scopus, IEEE Xplore, Web of Science, [...] Read more.
Processing and control systems based on artificial intelligence (AI) have progressively improved mobile robotic exoskeletons used in upper-limb motor rehabilitation. This systematic review presents the advances and trends of those technologies. A literature search was performed in Scopus, IEEE Xplore, Web of Science, and PubMed using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology with three main inclusion criteria: (a) motor or neuromotor rehabilitation for upper limbs, (b) mobile robotic exoskeletons, and (c) AI. The period under investigation spanned from 2016 to 2020, resulting in 30 articles that met the criteria. The literature showed the use of artificial neural networks (40%), adaptive algorithms (20%), and other mixed AI techniques (40%). Additionally, it was found that in only 16% of the articles, developments focused on neuromotor rehabilitation. The main trend in the research is the development of wearable robotic exoskeletons (53%) and the fusion of data collected from multiple sensors that enrich the training of intelligent algorithms. There is a latent need to develop more reliable systems through clinical validation and improvement of technical characteristics, such as weight/dimensions of devices, in order to have positive impacts on the rehabilitation process and improve the interactions among patients, teams of health professionals, and technology. Full article
(This article belongs to the Special Issue Electronics for E-health Sensor Systems)
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