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Electronics
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Biomedical Sensors and Systems for Medical Applications
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Biomedical Sensors and Systems for Medical Applications

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Bioelectronics".

Deadline for manuscript submissions: closed (20 April 2023) | Viewed by 3751

Special Issue Editors

Prof. Dr. Sohmyung Ha

E-Mail Website
Guest Editor
1. Department of Electrical and Computer Engineering, New York University Abu Dhabi, Abu Dhabi P.O. Box 129188, United Arab Emirates
2. Tandon School of Engineering, New York University, New York, NY 10003, USA
Interests: integrated circuits and systems for biomedical sensors and actuators; wearable and implantable devices; neural interface; wireless power transfer
Prof. Dr. Mario Muñoz Organero

E-Mail Website
Guest Editor
Telecommunications Engineering, Carlos III University of Madrid, 28911 Leganes, Spain
Interests: neural networks; artificial intelligence; computer supported learning; sensor-based applications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Medical devices capable of sensing various biomedical signals have emerged as a new breakthrough in the future of healthcare. Through recording and in situ processing of the biosignals, these devices can offer uninterrupted health monitoring and timely, personalized, and/or medical interventions.

The focus of this Special Issue is on recent advances in biomedical sensor developments and their interface electronic systems, which take the form of wearables, implants, and other various medical devices. Potential topics include (but are not limited to) biomedical sensor developments in various modalities such as electrochemical, kinetic, thermal, piezoelectric, photovoltaic, etc., sensor interface circuits and electronics; biomedical signal processing hardware; wearable and implantable systems utilizing biomedical sensors; and any applications of biomedical sensors in biomedical devices. We invite all of you to submit high-quality original research and review articles for publication in this Special Issue. 

Prof. Dr. Sohmyung Ha
Prof. Dr. Mario Muñoz Organero
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. Electronics 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 2400 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

  • biomedical sensors
  • biosignal sensing
  • physiological signal sensing
  • health monitoring
  • wearable devices
  • implantable devices
  • biomedical signal processing

Published Papers (2 papers)

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Research

16 pages, 5309 KiB  
Article
A 180 nm CMOS Integrated Optoelectronic Sensing System for Biomedical Applications
by Guido Di Patrizio Stanchieri, Andrea De Marcellis, Marco Faccio, Elia Palange, Graziano Battisti and Ulkuhan Guler
Electronics 2022, 11(23), 3952; https://doi.org/10.3390/electronics11233952 - 29 Nov 2022
Cited by 1 | Viewed by 1694
Abstract
This paper reports on a CMOS fully integrated optoelectronic sensing system composed of a Si photodiode and a transimpedance amplifier acting as the electronic analog front-end for the conditioning of the photocurrent generated by the photodiode. The proposed device has been specifically designed [...] Read more.
This paper reports on a CMOS fully integrated optoelectronic sensing system composed of a Si photodiode and a transimpedance amplifier acting as the electronic analog front-end for the conditioning of the photocurrent generated by the photodiode. The proposed device has been specifically designed and fabricated for wearable/portable/implantable biomedical applications. The massive employment of sensor systems in different industrial and medical fields requires the development of small sensing devices that, together with suitable electronic analog front ends, must be designed to be integrated into proper standard CMOS technologies. Concerning biomedical applications, these devices must be as small as possible, making them non-invasive, comfortable tools for patients and operating with a reduced supply voltage and power consumption. In this sense, optoelectronic solutions composed of a semiconductor light source and a photodiode fulfill these requirements while also ensuring high compatibility with biological tissues. The reported optoelectronic sensing system is implemented and fabricated in TSMC 180 nm integrated CMOS technology and combines a Si photodiode based on a PNP junction with a Si area of 0.01 mm2 and a transimpedance amplifier designed at a transistor level requiring a Si area of 0.002 mm2 capable to manage up to nanoampere input currents generated by the photodiode. The transimpedance amplifier is powered at a 1.8 V single supply showing a maximum power consumption of about 54 μW, providing a high transimpedance gain that is tunable up to 123 dBΩ with an associated bandwidth of about 500 kHz. The paper reports on both the working principle of the developed ASIC and the experimental measurements for its full electrical and optoelectronic characterizations. Moreover, as case-examples of biomedical applications, the proposed integrated sensing system has also been validated through the optical detection of emulated standard electrocardiography and photoplethysmography signal patterns. Full article
(This article belongs to the Special Issue Biomedical Sensors and Systems for Medical Applications)
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15 pages, 1011 KiB  
Article
A Hand Gesture Recognition Circuit Utilizing an Analog Voting Classifier
by Vassilis Alimisis, Vassilis Mouzakis, Georgios Gennis, Errikos Tsouvalas, Christos Dimas and Paul P. Sotiriadis
Electronics 2022, 11(23), 3915; https://doi.org/10.3390/electronics11233915 - 26 Nov 2022
Cited by 8 | Viewed by 1471
Abstract
Electromyography is a diagnostic medical procedure used to assess the state of a muscle and its related nerves. Electromyography signals are monitored to detect neuromuscular abnormalities and diseases but can also prove useful in decoding movement-related signals. This information is vital to controlling [...] Read more.
Electromyography is a diagnostic medical procedure used to assess the state of a muscle and its related nerves. Electromyography signals are monitored to detect neuromuscular abnormalities and diseases but can also prove useful in decoding movement-related signals. This information is vital to controlling prosthetics in a more natural way. To this end, a novel analog integrated voting classifier is proposed as a hand gesture recognition system. The voting classifiers utilize 3 separate centroid-based classifiers, each one attached to a different electromyographic electrode and a voting circuit. The main building blocks of the architecture are bump and winner-take-all circuits. To confirm the proper operation of the proposed classifier, its post-layout classification results (91.2% accuracy) are compared to a software-based implementation (93.8% accuracy) of the same voting classifier. A TSMC 90 nm CMOS process in the Cadence IC Suite was used to design and simulate the following circuits and architectures. Full article
(This article belongs to the Special Issue Biomedical Sensors and Systems for Medical Applications)
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