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Bioelectronics in Korea - Emerging Medical Electronics and Digital Healthcare...
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Bioelectronics in Korea - Emerging Medical Electronics and Digital Healthcare Technologies

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

Deadline for manuscript submissions: closed (31 July 2023) | Viewed by 38233

Special Issue Editors

Prof. Dr. Hyun Jae Baek

E-Mail Website
Guest Editor
Department of Biomedical Engineering, Soonchunhyang University, Asan-si 31538, Republic of Korea
Interests: medical electronics; health IoT; sleep engineering; brain–computer interface; digital therapeutics
Special Issues, Collections and Topics in MDPI journals
Dr. Kwang Bok Kim

E-Mail Website
Guest Editor
Digital Health Care R&D Department, Korea Institute of Industrial Technology, Cheonan, Republic of Korea
Interests: electrochemical sensor; glucose sensor; iontronics; preconcentration
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Today's healthcare environment is changing very rapidly, and the role of medical electronics in healthcare domain is growing day by day. In particular, as the public's interest in the fourth industrial revolution in the medical field and government support increases, the scale of Korea’s digital healthcare industry, based on advanced medical electronic technology, is gradually growing. In addition, the market size is expected to grow further due to the increase in income level and the progression into an aging society.

This Special Issue is focused on state-of-the-art medical electronics in Korea. It will include novel research results about technologies, such as biomedical sensor, algorithm, healthcare IoT devices, including portable/wearable/attachable device, digital therapeutics, and electroceutical. Attention will also be paid to their various industry applications.

The topics of interest include, but are not limited to, the following:

  • Biomedical sensor and actuator;
  • New concept biomedical devices such as wearable/nearable/attachable devices;
  • Biomedical signal processing;
  • Health and wellness sensing algorithm;
  • Artificial intelligence in biomedical engineering;
  • Software based medical device;
  • Digital therapeutics and electroceutical;
  • Clinical applications of biomedical electronics.

Prof. Dr. Hyun Jae Baek
Dr. Kwang Bok Kim
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.

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

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14 pages, 4386 KiB  
Article
RGB Camera-Based Blood Pressure Measurement Using U-Net Basic Generative Model
by Seunghyun Kim, Hyeji Lim, Junho Baek and Eui Chul Lee
Electronics 2023, 12(18), 3771; https://doi.org/10.3390/electronics12183771 - 6 Sep 2023
Cited by 3 | Viewed by 3721
Abstract
Blood pressure is a fundamental health metric widely employed to predict cardiac diseases and monitor overall well-being. However, conventional blood pressure measurement methods, such as the cuff method, necessitate additional equipment and can be inconvenient for regular use. This study aimed to develop [...] Read more.
Blood pressure is a fundamental health metric widely employed to predict cardiac diseases and monitor overall well-being. However, conventional blood pressure measurement methods, such as the cuff method, necessitate additional equipment and can be inconvenient for regular use. This study aimed to develop a novel approach to blood pressure measurement using only an RGB camera, which promises enhanced convenience and accuracy. We employed the U-Net Basic generative model to achieve our objectives. Through rigorous experimentation and data analysis, our approach demonstrated promising results, attaining BHS (British Hypertension Society) baseline performance with grade A accuracy for diastolic blood pressure (DBP) and grade C accuracy for systolic blood pressure (SBP). The mean absolute error (MAE) achieved for DBP was 4.43 mmHg, and for SBP, it was 6.9 mmHg. Our findings indicate that blood pressure measurement using an RGB camera shows significant potential and may be utilized as an alternative or supplementary method for blood pressure monitoring. The convenience of using a commonly available RGB camera without additional specialized equipment can empower individuals to track their blood pressure regularly and proactively predict potential heart-related issues. Full article
(This article belongs to the Special Issue Bioelectronics in Korea - Emerging Medical Electronics and Digital Healthcare Technologies)
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19 pages, 11519 KiB  
Article
EMG-Based Dynamic Hand Gesture Recognition Using Edge AI for Human–Robot Interaction
by EunSu Kim, JaeWook Shin, YongSung Kwon and BumYong Park
Electronics 2023, 12(7), 1541; https://doi.org/10.3390/electronics12071541 - 24 Mar 2023
Cited by 22 | Viewed by 6440
Abstract
Recently, human–robot interaction technology has been considered as a key solution for smart factories. Surface electromyography signals obtained from hand gestures are often used to enable users to control robots through hand gestures. In this paper, we propose a dynamic hand-gesture-based industrial robot [...] Read more.
Recently, human–robot interaction technology has been considered as a key solution for smart factories. Surface electromyography signals obtained from hand gestures are often used to enable users to control robots through hand gestures. In this paper, we propose a dynamic hand-gesture-based industrial robot control system using the edge AI platform. The proposed system can perform both robot operating-system-based control and edge AI control through an embedded board without requiring an external personal computer. Systems on a mobile edge AI platform must be lightweight, robust, and fast. In the context of a smart factory, classifying a given hand gesture is important for ensuring correct operation. In this study, we collected electromyography signal data from hand gestures and used them to train a convolutional recurrent neural network. The trained classifier model achieved 96% accuracy for 10 gestures in real time. We also verified the universality of the classifier by testing it on 11 different participants. Full article
(This article belongs to the Special Issue Bioelectronics in Korea - Emerging Medical Electronics and Digital Healthcare Technologies)
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Review

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24 pages, 2585 KiB  
Review
Photoplethysmography in Wearable Devices: A Comprehensive Review of Technological Advances, Current Challenges, and Future Directions
by Kwang Bok Kim and Hyun Jae Baek
Electronics 2023, 12(13), 2923; https://doi.org/10.3390/electronics12132923 - 3 Jul 2023
Cited by 66 | Viewed by 28025
Abstract
Photoplethysmography (PPG) is an affordable and straightforward optical technique used to detect changes in blood volume within tissue microvascular beds. PPG technology has found widespread application in commercial medical devices, enabling measurements of oxygen saturation, blood pressure, and cardiac output; the assessment of [...] Read more.
Photoplethysmography (PPG) is an affordable and straightforward optical technique used to detect changes in blood volume within tissue microvascular beds. PPG technology has found widespread application in commercial medical devices, enabling measurements of oxygen saturation, blood pressure, and cardiac output; the assessment of autonomic nerve function; and the diagnosis of peripheral vascular disease. Recently, the growing demand for non-invasive, portable, cost-effective technology, along with advancements in small semiconductor components, has led to the integration of PPG into various wrist-worn wearable devices. Multiple sensor structures have been proposed and, through appropriate signal processing and algorithmic application, these wearable devices can measure a range of health indicators during daily life. This paper begins by addressing the market status of wrist-worn wearable devices, followed by an explanation of the fundamental principles underlying light operation and its interaction with living tissue for PPG measurements. Moving on to technological advancements, the paper addresses the analog front end for the measurement of the PPG signal, sensor configurations with multiple light emitters and receivers, the minimum sampling rate required for low-power systems, and the measurement of stress, sleep, blood pressure, blood glucose, and activity using PPG signals. Several challenges in the field are also identified, including selecting the appropriate wavelength for the PPG sensor’s light source, developing low-power interpolation methods to extract high-resolution inter-beat intervals at a low sampling rate, and exploring the measurement of physiological phenomena using multi-wavelength PPG signals simultaneously collected at the same location. Lastly, the paper presents future research directions, which encompass the development of new, reliable parameters specific to wearable PPG devices and conducting studies in real-world scenarios, such as 24-h long-term measurements. Full article
(This article belongs to the Special Issue Bioelectronics in Korea - Emerging Medical Electronics and Digital Healthcare Technologies)
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