10th Anniversary of Electronics: Hot Topics in Bioelectronics

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

Deadline for manuscript submissions: closed (31 March 2022) | Viewed by 23230

Special Issue Editors


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Guest Editor
Department of Information Engineering, School of Engineering, University of Pisa, 56126 Pisa, PI, Italy
Interests: wearable monitoring systems; affective computing; heart rate variability; human-computer interfaces; biomedical and biomechanical signal processing; modeling; control and instrumentation; autonomic nervous system
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Information Engineering, Università degli Studi di Firenze, Firenze, Italy
Interests: wearable system for non-invasive physiological monitoring; statistical and nonlinear biomedical signal processing; affective computing; mood/mental/neurological disorders; human–animal–robot interaction; autonomic nervous system investigation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues, 

First published in 2011, we now are proud to celebrate the 10th anniversary of Electronics. To mark this occasion, a Special Issue dedicated to the area of “Bioelectronics” is being prepared. Past Editors and authors will be invited to submit high-quality papers to this Special Issue. Topics of interest include, but are not limited to, the following:

  • Biosignal acquisition/conditioning/detection;
  • Miniaturized electronics;
  • Wearable and implantable sensors;
  • Neuromodulation and -stimulation;
  • Transcutaneous and intrabody wireless communication;
  • Wireless power transfer and management;
  • Energy harvesting;
  • Bioinspired electronics;
  • Implantable electronics;
  • Wireless CMOS biosensors and sensor technology;
  • Multimodal healthcare sensors;
  • Mobile and lab-on-a-chip healthcare microsystems;
  • Unobtrusive biosensing;
  • Rehabilitation robotics;
  • Artificial intelligence for health management;
  • Bioinformatics for healthcare engineering;
  • Medical data mining and big data analytics;
  • Ethical aspects of bioelectronics.

Prof. Dr. Enzo Pasquale Scilingo
Prof. Dr. Nicola Vanello
Prof. Dr. Antonio Lanata
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.

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Keywords

  • Biosignal acquisition/conditioning/detection
  • Miniaturized electronics
  • Wearable and implantable sensors
  • Neuromodulation and -stimulation
  • Transcutaneous and intrabody wireless communication
  • Wireless power transfer and management
  • Energy harvesting
  • Bioinspired electronics
  • Implantable electronics
  • Wireless CMOS biosensors and sensor technology
  • Multimodal healthcare sensors
  • Mobile and lab-on-a-chip healthcare microsystems
  • Unobtrusive biosensing
  • Rehabilitation robotics
  • Artificial intelligence for health management
  • Bioinformatics for healthcare engineering
  • Medical data mining and big data analytics
  • Ethical aspects of bioelectronics.

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Published Papers (5 papers)

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Research

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13 pages, 2985 KiB  
Article
A Miniaturized Quartz Crystal Microbalance (QCM) Measurement Instrument Based on a Phase-Locked Loop Circuit
by Jong-Yoon Park, Rocío L. Pérez, Caitlan E. Ayala, Stephanie R. Vaughan, Isiah M. Warner and Jin-Woo Choi
Electronics 2022, 11(3), 358; https://doi.org/10.3390/electronics11030358 - 25 Jan 2022
Cited by 10 | Viewed by 4412
Abstract
The quartz crystal microbalance (QCM) has been widely used in laboratory settings as an analytical tool for recognizing and discriminating biological and chemical molecules of interest. As a result, recent studies have shown there to be considerable attention in practical applications of the [...] Read more.
The quartz crystal microbalance (QCM) has been widely used in laboratory settings as an analytical tool for recognizing and discriminating biological and chemical molecules of interest. As a result, recent studies have shown there to be considerable attention in practical applications of the QCM technique beyond the laboratory. However, most commercial QCM instruments are not suitable for off-laboratory usage. For field-deployable applications and in situ detection, the development of a portable QCM measurement system achieving comparable performance to benchtop instruments is highly desired. In this paper, we describe the development of a fully customizable, miniaturized, battery-powered, and cost-efficient QCM system employing a phase-locked loop (PLL) electronic circuit-based QCM measurement system. The performance of this developed system showed a minimum frequency resolution of approximately 0.22 Hz at 0.1 s measurement time. This novel, miniaturized system successfully demonstrated an ability to detect two common volatile organic compounds (VOCs), methanol and dichloromethane (DCM), and the obtained results were comparable to responses from a commercially available benchtop instrument. Full article
(This article belongs to the Special Issue 10th Anniversary of Electronics: Hot Topics in Bioelectronics)
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8 pages, 3349 KiB  
Article
Wearable Flexible Phototherapy Device for Knee Osteoarthritis
by Kun Liu, Hongda Chen, Yuguang Wang, Mengqi Wang and Jun Tang
Electronics 2021, 10(16), 1891; https://doi.org/10.3390/electronics10161891 - 6 Aug 2021
Cited by 4 | Viewed by 2906
Abstract
Knee osteoarthritis (OA) is a highly prevalent and disabling disease that causes pain and gradual degeneration of the articular cartilage. Phototherapy is a new physiotherapy treatment, more effective and stable than other non-pharmacologic management. Conventional phototherapy devices typically suffer from unintelligent and bulky [...] Read more.
Knee osteoarthritis (OA) is a highly prevalent and disabling disease that causes pain and gradual degeneration of the articular cartilage. Phototherapy is a new physiotherapy treatment, more effective and stable than other non-pharmacologic management. Conventional phototherapy devices typically suffer from unintelligent and bulky equipment, while existing phototherapy methods require maintain a certain phototherapy distance. Here, we introduce a wearable flexible phototherapy device worn on a knee for osteoarthritis, incorporating a phototherapy adhesive patch and a control box. The phototherapy adhesive patch is capable of softly laminating onto the curved surfaces of the knee skin to increase the effects of phototherapy. We describe the LED array, constant current drive module, key control module, and power supply module that serve as the foundations for the control box. The weight of the device is only 101.8 g. The irradiance of the device can be adjusted linearly and irradiance of the designed phototherapy device based on LED can reach 13 mW/cm2. The maximum temperature of the surface of the light source is 31.2 °C. The device proposed in this work exhibits satisfactory stability, promising a potential in phototherapy. Full article
(This article belongs to the Special Issue 10th Anniversary of Electronics: Hot Topics in Bioelectronics)
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11 pages, 3549 KiB  
Article
A Short Survey on Machine Learning Explainability: An Application to Periocular Recognition
by João Brito and Hugo Proença
Electronics 2021, 10(15), 1861; https://doi.org/10.3390/electronics10151861 - 3 Aug 2021
Cited by 6 | Viewed by 2394
Abstract
Interpretability has made significant strides in recent years, enabling the formerly black-box models to reach new levels of transparency. These kinds of models can be particularly useful to broaden the applicability of machine learning-based systems to domains where—apart from the predictions—appropriate justifications are [...] Read more.
Interpretability has made significant strides in recent years, enabling the formerly black-box models to reach new levels of transparency. These kinds of models can be particularly useful to broaden the applicability of machine learning-based systems to domains where—apart from the predictions—appropriate justifications are also required (e.g., forensics and medical image analysis). In this context, techniques that focus on visual explanations are of particular interest here, due to their ability to directly portray the reasons that support a given prediction. Therefore, in this document, we focus on presenting the core principles of interpretability and describing the main methods that deliver visual cues (including one that we designed for periocular recognition in particular). Based on these intuitions, the experiments performed show explanations that attempt to highlight the most important periocular components towards a non-match decision. Then, some particularly challenging scenarios are presented to naturally sustain our conclusions and thoughts regarding future directions. Full article
(This article belongs to the Special Issue 10th Anniversary of Electronics: Hot Topics in Bioelectronics)
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10 pages, 6654 KiB  
Communication
Efficacy of Smart EEG Monitoring Amidst the COVID-19 Pandemic
by Misagh Faezipour and Miad Faezipour
Electronics 2021, 10(9), 1001; https://doi.org/10.3390/electronics10091001 - 22 Apr 2021
Cited by 12 | Viewed by 2859
Abstract
Ever since the COVID-19 pandemic has majorly altered diagnosis and prognosis practices, the need for telemedicine and mobile/electronic health has never been more appreciated. Drastic complications of the pandemic such as burdens on the social and employment status resulting from extended quarantine and [...] Read more.
Ever since the COVID-19 pandemic has majorly altered diagnosis and prognosis practices, the need for telemedicine and mobile/electronic health has never been more appreciated. Drastic complications of the pandemic such as burdens on the social and employment status resulting from extended quarantine and physical distancing, has also negatively impacted mental health. Doctors and healthcare workers have seen more than just the lungs affected by COVID-19. Neurological complications including stroke, headache, and seizures have been reported for populations of patients. Most mental conditions can be detected using the Electroencephalogram (EEG) signal. Brain disorders, neurodegenerative diseases, seizure/epilepsy, sleep/fatigue, stress, and depression have certain characteristics in the EEG wave, which clearly differentiate them from normal conditions. Smartphone apps analyzing the EEG signal have been introduced in the market. However, the efficacy of such apps has not been thoroughly investigated. Factors and their inter-relationships impacting efficacy can be studied through a causal model. This short communications/perspective paper outlines the initial premises of a system dynamics approach to assess the efficacy of smart EEG monitoring apps amid the pandemic, that could be revolutionary for patient well-being and care policies. Full article
(This article belongs to the Special Issue 10th Anniversary of Electronics: Hot Topics in Bioelectronics)
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Review

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36 pages, 25517 KiB  
Review
An Overview of Wearable Piezoresistive and Inertial Sensors for Respiration Rate Monitoring
by Roberto De Fazio, Marco Stabile, Massimo De Vittorio, Ramiro Velázquez and Paolo Visconti
Electronics 2021, 10(17), 2178; https://doi.org/10.3390/electronics10172178 - 6 Sep 2021
Cited by 46 | Viewed by 9124
Abstract
The demand for wearable devices to measure respiratory activity is constantly growing, finding applications in a wide range of scenarios (e.g., clinical environments and workplaces, outdoors for monitoring sports activities, etc.). Particularly, the respiration rate (RR) is a vital parameter since it indicates [...] Read more.
The demand for wearable devices to measure respiratory activity is constantly growing, finding applications in a wide range of scenarios (e.g., clinical environments and workplaces, outdoors for monitoring sports activities, etc.). Particularly, the respiration rate (RR) is a vital parameter since it indicates serious illness (e.g., pneumonia, emphysema, pulmonary embolism, etc.). Therefore, several solutions have been presented in the scientific literature and on the market to make RR monitoring simple, accurate, reliable and noninvasive. Among the different transduction methods, the piezoresistive and inertial ones satisfactorily meet the requirements for smart wearable devices since unobtrusive, lightweight and easy to integrate. Hence, this review paper focuses on innovative wearable devices, detection strategies and algorithms that exploit piezoresistive or inertial sensors to monitor the breathing parameters. At first, this paper presents a comprehensive overview of innovative piezoresistive wearable devices for measuring user’s respiratory variables. Later, a survey of novel piezoresistive textiles to develop wearable devices for detecting breathing movements is reported. Afterwards, the state-of-art about wearable devices to monitor the respiratory parameters, based on inertial sensors (i.e., accelerometers and gyroscopes), is presented for detecting dysfunctions or pathologies in a non-invasive and accurate way. In this field, several processing tools are employed to extract the respiratory parameters from inertial data; therefore, an overview of algorithms and methods to determine the respiratory rate from acceleration data is provided. Finally, comparative analysis for all the covered topics are reported, providing useful insights to develop the next generation of wearable sensors for monitoring respiratory parameters. Full article
(This article belongs to the Special Issue 10th Anniversary of Electronics: Hot Topics in Bioelectronics)
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