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Electronics
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Bio-Information and Human Body Communication for Ubiquitous Healthcare
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Bio-Information and Human Body Communication for Ubiquitous Healthcare

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

Deadline for manuscript submissions: closed (30 September 2022) | Viewed by 18531

Special Issue Editors

Prof. Dr. Youn Tae Kim

E-Mail Website
Guest Editor
Department of Electronic Engineering, Chosun University, Gwangju 61452, Republic of Korea
Interests: wearable devices for ubiquitous healthcare; bio-information and communication; human body communication and power transmission; energy harvesting devices
Dr. Junghwan Hwang

E-Mail Website
Guest Editor
Electronics and Telecommunications Research Institute, Daejeon, Korea
Interests: wireless body area network (WBAN); human body communication; electromagnetic compatibility; electromagnetic safety; high power electromagnetics
Prof. Dr. Meina Li

E-Mail Website
Guest Editor
Department of Instrument Science and Technology, Jilin University, 938 West Democracy Avenue, Changchun City, Jilin Province, China
Interests: wireless bio-sensors system design and application; BSN; bio-sensor; human body communication

Special Issue Information

Dear Colleagues,

Ubiquitous healthcare is an important field that provides value-based care to millions of people while generating high revenues in many countries. This technology can provide a scalable solution to prevent severe disease by predicting early disease remotely, beyond the scope of diagnosis. In addition, human body communication is a field in which a cable that connects terminals is not required by utilizing the human body as a medium for signals, and high-speed data transmission is possible with low power. When this technology is combined with the healthcare field, a network that integrates and manages various sensors can be formed to manage extensive information with a low-power module, thereby providing better quality services. This Special Issue welcomes contributions from all of our industry peers to cover the latest developments in ubiquitous healthcare, wearable sensors, bio-signal data transmission through human body communication, and a variety of similar topics.

Prof. Dr. Youn Tae Kim
Dr. Junghwan Hwang
Prof. Dr. Meina Li
Guest Editors

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Keywords

  • wearable wireless sensor for signal acquisition
  • medical applications of pattern recognition based on wearable sensor data
  • bio-signal processing and analysis
  • AI-assisted IoT data analysis for smart healthcare
  • IoT cloud-based predictive analytics for personalized healthcare
  • implantable sensors for healthcare application
  • security and reliability enhancement for healthcare communication
  • designing and manufacturing components for healthcare sensors
  • application using human body communication
  • channel measurement and modeling of human body communication
  • wireless body area network for implantable devices
  • channel analysis for wireless body area network
  • in-situ and laboratory measurement of wireless body area network
  • electromagnetic compatibility (EMC) and electromagnetic interference (EMI) in communication between healthcare sensors
  • miniaturization and low-power consumption design of devices for wireless body area network

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Published Papers (5 papers)

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Research

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15 pages, 4985 KiB  
Article
A Study on the Relationship between RPE and sEMG in Dynamic Contraction Based on the GPR Method
by Weiguang Ni, Yuxin Zhang, Xinyi Li, Xixi Wang, Yiqi Wu and Guangda Liu
Electronics 2022, 11(5), 691; https://doi.org/10.3390/electronics11050691 - 24 Feb 2022
Cited by 5 | Viewed by 2048
Abstract
The rating of perceived exertion (RPE) and surface electromyography (sEMG) describe exercise intensity subjectively and objectively, while there has been a lack of research on the relationship between them during dynamic contractions to predict exercise intensity, comprehensively. The purpose of this study was [...] Read more.
The rating of perceived exertion (RPE) and surface electromyography (sEMG) describe exercise intensity subjectively and objectively, while there has been a lack of research on the relationship between them during dynamic contractions to predict exercise intensity, comprehensively. The purpose of this study was to establish a model of the relationship between sEMG and RPE during dynamic exercises. Therefore, 20 healthy male subjects were organized to perform an incremental load test on a cycle ergometer, and the subjects’ RPEs (Borg Scale 6–20) were collected every minute. Additionally, the sEMGs of the subjects’ eight lower limb muscles were collected. The sEMG features based on time domain, frequency domain and time–frequency domain methods were extracted, and the relationship model was established using Gaussian process regression (GPR). The results show that the sEMG and RPE of the selected lower limb muscles are significantly correlated (p < 0.05) but that they have different monotonic correlation degrees. The model that was established with all three domain features displayed optimal performance and when the RPE was 13, the prediction error was the smallest. The study is significant for lower limb muscle training strategy and quantification of training intensity from both subjective and objective aspects, and lays a foundation for sEMG further applications in rehabilitation medicine and sports training. Full article
(This article belongs to the Special Issue Bio-Information and Human Body Communication for Ubiquitous Healthcare)
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13 pages, 6322 KiB  
Article
Input Impedance Analysis of Wearable Antenna and Experimental Study with Real Human Subjects: Differences between Individual Users
by Dairoku Muramatsu and Ken Sasaki
Electronics 2021, 10(10), 1152; https://doi.org/10.3390/electronics10101152 - 12 May 2021
Cited by 4 | Viewed by 3729
Abstract
In human body communication (HBC) systems, radio-frequency signals are excited in the human body through a wearable antenna comprised of electrodes that are in contact with the surface of the body. The input impedance characteristics of these antennas are important design parameters for [...] Read more.
In human body communication (HBC) systems, radio-frequency signals are excited in the human body through a wearable antenna comprised of electrodes that are in contact with the surface of the body. The input impedance characteristics of these antennas are important design parameters for increasing transmission efficiency and reducing signal reflection, similar to other wireless circuits. In this study, we discuss variations of input impedance characteristics of a wearable antenna prototype caused by differences among real human subjects. A realistic human arm model is used for simulations, and the analytical results obtained are compared to measured data obtained from real human subjects, in a range from 1 to 100 MHz. The simulations of input impedance characteristics from antennas worn on the wrists of male and female models with dry and wet skin conditions show that the impedance variation between genders is small. The moisture condition of the skin has little influence on frequencies exceeding several MHz. Measurements with a proto-type wearable antenna and 22 real human subjects reveal that HBC is robust against the variations of individual users from the viewpoint of the voltage standing wave ratio. Moreover, a simplified rectangular prism model is proposed to analyze the thickness of body tissues. Comparisons of measured input impedances indicate that individual differences in impedance are mainly due to differences in the thickness of skin and fat layers. The model also enables us to design the antenna prototype without multiple subject experiments. Full article
(This article belongs to the Special Issue Bio-Information and Human Body Communication for Ubiquitous Healthcare)
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12 pages, 2173 KiB  
Article
Estimating Physical Activity Energy Expenditure Using an Ensemble Model-Based Patch-Type Sensor Module
by Kyeung Ho Kang, Mingu Kang, Siho Shin, Jaehyo Jung and Meina Li
Electronics 2021, 10(7), 861; https://doi.org/10.3390/electronics10070861 - 5 Apr 2021
Viewed by 2091
Abstract
Chronic diseases, such as coronary artery disease and diabetes, are caused by inadequate physical activity and are the leading cause of increasing mortality and morbidity rates. Direct calorimetry by calorie production and indirect calorimetry by energy expenditure (EE) has been regarded as the [...] Read more.
Chronic diseases, such as coronary artery disease and diabetes, are caused by inadequate physical activity and are the leading cause of increasing mortality and morbidity rates. Direct calorimetry by calorie production and indirect calorimetry by energy expenditure (EE) has been regarded as the best method for estimating the physical activity and EE. However, this method is inconvenient, owing to the use of an oxygen respiration measurement mask. In this study, we propose a model that estimates physical activity EE using an ensemble model that combines artificial neural networks and genetic algorithms using the data acquired from patch-type sensors. The proposed ensemble model achieved an accuracy of more than 92% (Root Mean Squared Error (RMSE) = 0.1893, R2 = 0.91, Mean Squared Error (MSE) = 0.014213, Mean Absolute Error (MAE) = 0.14020) by testing various structures through repeated experiments. Full article
(This article belongs to the Special Issue Bio-Information and Human Body Communication for Ubiquitous Healthcare)
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11 pages, 8746 KiB  
Article
Development of Wearable Wireless Electrocardiogram Detection System using Bluetooth Low Energy
by Jaehyo Jung, Siho Shin, Mingu Kang, Kyeung Ho Kang and Youn Tae Kim
Electronics 2021, 10(5), 608; https://doi.org/10.3390/electronics10050608 - 5 Mar 2021
Cited by 12 | Viewed by 5843
Abstract
Wearable monitoring devices can provide patients and doctors with the capability to measure bio-signals on demand. These systems provide enormous benefits for people with acute symptoms of serious health conditions. In this paper, we propose a novel method for collecting ECG signals using [...] Read more.
Wearable monitoring devices can provide patients and doctors with the capability to measure bio-signals on demand. These systems provide enormous benefits for people with acute symptoms of serious health conditions. In this paper, we propose a novel method for collecting ECG signals using two wireless wearable modules. The electric potential measured from a sub-module is transferred to the main module through Bluetooth Low Energy, and the collected values are simultaneously displayed in the form of a graph. This study describes the configuration and outcomes of the proposed system and discusses the important challenges associated with the functioning of the device. The proposed system had 84% signal similarity to that of other commercial products. As a band-type module was used on each wrist to check the signal, continuous observation of patients can be achieved without restricting their actions or causing discomfort. Full article
(This article belongs to the Special Issue Bio-Information and Human Body Communication for Ubiquitous Healthcare)
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Review

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24 pages, 6723 KiB  
Review
Sodium Radiofrequency Coils for Magnetic Resonance: From Design to Applications
by Giulio Giovannetti, Alessandra Flori, Nicola Martini, Roberto Francischello, Giovanni Donato Aquaro, Alessandro Pingitore and Francesca Frijia
Electronics 2021, 10(15), 1788; https://doi.org/10.3390/electronics10151788 - 26 Jul 2021
Cited by 7 | Viewed by 3445
Abstract
Sodium (23Na) is the most abundant cation present in the human body and is involved in a large number of vital body functions. In the last few years, the interest in Sodium Magnetic Resonance Imaging (23Na MRI) has considerably [...] Read more.
Sodium (23Na) is the most abundant cation present in the human body and is involved in a large number of vital body functions. In the last few years, the interest in Sodium Magnetic Resonance Imaging (23Na MRI) has considerably increased for its relevance in physiological and physiopathological aspects. Indeed, sodium MRI offers the possibility to extend the anatomical imaging information by providing additional and complementary information on physiology and cellular metabolism with the heteronuclear Magnetic Resonance Spectroscopy (MRS). Constraints are the rapidly decaying of sodium signal, the sensitivity lack due to the low sodium concentration versus 1H-MRI induce scan times not clinically acceptable and it also constitutes a challenge for sodium MRI. With the available magnetic fields for clinical MRI scanners (1.5 T, 3 T, 7 T), and the hardware capabilities such as strong gradient strengths with high slew rates and new dedicated radiofrequency (RF) sodium coils, it is possible to reach reasonable measurement times (~10–15 min) with a resolution of a few millimeters, where it has already been applied in vivo in many human organs such as the brain, cartilage, kidneys, heart, as well as in muscle and the breast. In this work, we review the different geometries and setup of sodium coils described in the available literature for different in vivo applications in human organs with clinical MR scanners, by providing details of the design, modeling and construction of the coils. Full article
(This article belongs to the Special Issue Bio-Information and Human Body Communication for Ubiquitous Healthcare)
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