Special Issue "Electronic Solutions for Artificial Intelligence Healthcare"

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

Deadline for manuscript submissions: 30 November 2019.

Special Issue Editor

Guest Editor
Prof. Dr. Jun-Ho Huh

Department of Software, Catholic University of Pusan, 57 Oryundae-ro, Bugok 3(sam)-dong, Geumjeong-gu, Busan, Korea
Website 1 | Website 2 | E-Mail
Interests: smart grid, medical IT, network security, energy harvesting, optimization

Special Issue Information

Dear Colleagues,

Currently, diverse, innovative technology is being used in electronics and ubiquitous computing environments. This allows us to create a better world by providing the backbone for remarkable development in our human society in the fields of electronics, devices, computer science, and engineering. Healthcare and bioelectronics in artificial intelligence are becoming more and more complex and sophisticated faster than ever before.

Thus, in this SI, we aim to start a discussion about a basic convergent study that would contribute to humanity by respecting human beings and their lives, while aiding and serving neglected or isolated people. For this purpose, this Special Issue is open to receiving a variety of meaningful and valuable manuscripts concerning the purpose of solving the healthcare issue based on electronic solutions. Participants may write about one of the subjects listed below, but they are not limited to them.

> Electronic service respecting human beings and their lives;

> Electronic solutions to artificial intelligence and Big Data;

> Means of aiding and serving neglected people like the disabled or elderly;

> Electronic engineering mathematical theories that deeply affect science and industry;

> Intelligent media techniques and services for systems engineering;

> A public electronic engineering integration system for the future systems;

Prof. Dr. Jun-Ho Huh
Guest Editor

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 papers will be 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 monthly 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 1400 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

  • Humanity Solution
  • Artificial Intelligence
  • Application
  • Big Data
  • Intelligent media techniques
  • Mathematical theories
  • Healthcare

Published Papers (5 papers)

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Research

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Open AccessFeature PaperArticle
A Mechanism of Masking Identification Information regarding Moving Objects Recorded on Visual Surveillance Systems by Differentially Implementing Access Permission
Electronics 2019, 8(7), 735; https://doi.org/10.3390/electronics8070735
Received: 13 May 2019 / Revised: 24 June 2019 / Accepted: 26 June 2019 / Published: 28 June 2019
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Abstract
Video surveillance systems (VSS), used as a measure of security strengthening as well as investigation, are provided principally in heavily crowded public places. They record images of moving objects and transmit them to the control center. Typically, the recorded images are stored after [...] Read more.
Video surveillance systems (VSS), used as a measure of security strengthening as well as investigation, are provided principally in heavily crowded public places. They record images of moving objects and transmit them to the control center. Typically, the recorded images are stored after being encrypted, or masked using visual obfuscations on a concerned image(s) in the identification-enabling data contained in the visual information. The stored footage is recovered to its original state by authorized users. However, the recovery entails the restoration of all information in the visual data, possibly infiltrating the privacy of the object(s) other than the one(s) whose images are requested. In particular, Artificial Intelligence Healthcare that checks the health status of an object through images has the same problem and must protect the patient’s identification information. This study proposes a masking mechanism wherein the infiltration of visual data privacy on videos is minimized by limiting the objects whose images are recovered with differential use of access permission granted to the requesting users. Full article
(This article belongs to the Special Issue Electronic Solutions for Artificial Intelligence Healthcare)
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Open AccessArticle
Improved Heart-Rate Measurement from Mobile Face Videos
Electronics 2019, 8(6), 663; https://doi.org/10.3390/electronics8060663
Received: 9 May 2019 / Revised: 10 June 2019 / Accepted: 10 June 2019 / Published: 12 June 2019
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Abstract
Newtonian reaction to blood influx into the head at each heartbeat causes subtle head motion at the same frequency as the heartbeats. Thus, this head motion can be used to estimate the heart rate. Several studies have shown that heart rates can be [...] Read more.
Newtonian reaction to blood influx into the head at each heartbeat causes subtle head motion at the same frequency as the heartbeats. Thus, this head motion can be used to estimate the heart rate. Several studies have shown that heart rates can be measured accurately by tracking head motion using a desktop computer with a static camera. However, implementation of vision-based head motion tracking on smartphones demonstrated limited accuracy due to the hand-shaking problem caused by the non-static camera. The hand-shaking problem could not be handled effectively with only the frontal camera images. It also required a more accurate method to measure the periodicity of noisy signals. Therefore, this study proposes an improved head-motion-based heart-rate monitoring system using smartphones. To address the hand-shaking problem, the proposed system leverages the front and rear cameras available in most smartphones and dedicates each camera to tracking facial features that correspond to head motion and background features that correspond to hand-shaking. Then, the locations of facial features are adjusted using the average point of the background features. In addition, a correlation-based signal periodicity computation method is proposed to accurately separate the true heart-rate-related component from the head motion signal. The proposed system demonstrates improved accuracy (i.e., lower mean errors in heart-rate measurement) compared to conventional head-motion-based systems, and the accuracy is sufficient for daily heart-rate monitoring. Full article
(This article belongs to the Special Issue Electronic Solutions for Artificial Intelligence Healthcare)
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Open AccessArticle
Indoor Positioning System: A New Approach Based on LSTM and Two Stage Activity Classification
Electronics 2019, 8(4), 375; https://doi.org/10.3390/electronics8040375
Received: 20 February 2019 / Revised: 18 March 2019 / Accepted: 21 March 2019 / Published: 28 March 2019
Cited by 1 | PDF Full-text (2520 KB) | HTML Full-text | XML Full-text
Abstract
The number of studies on the development of indoor positioning systems has increased recently due to the growing demands of the various location-based services. Inertial sensors available in commercial smartphones play an important role in indoor localization and navigation owing to their highly [...] Read more.
The number of studies on the development of indoor positioning systems has increased recently due to the growing demands of the various location-based services. Inertial sensors available in commercial smartphones play an important role in indoor localization and navigation owing to their highly accurate localization performance. In this study, the inertial sensors of a smartphone, which generate distinct patterns for physical activities and action units (AUs), are employed to localize a target in an indoor environment. These AUs, (such as a left turn, right turn, normal step, short step, or long step), help to accurately estimate the indoor location of a target. By taking advantage of sophisticated deep learning algorithms, we propose a novel approach for indoor navigation based on long short-term memory (LSTM). The LSTM accurately recognizes physical activities and related AUs by automatically extracting the efficient features from the distinct patterns of the input data. Experiment results show that LSTM provides a significant improvement in the indoor positioning performance through the recognition task. The proposed system achieves a better localization performance than the trivial fingerprinting method, with an average error of 0.782 m in an indoor area of 128.6 m2. Additionally, the proposed system exhibited robust performance by excluding the abnormal activity from the pedestrian activities. Full article
(This article belongs to the Special Issue Electronic Solutions for Artificial Intelligence Healthcare)
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Open AccessArticle
Multilayer Perceptron Neural Network-Based QoS-Aware, Content-Aware and Device-Aware QoE Prediction Model: A Proposed Prediction Model for Medical Ultrasound Streaming Over Small Cell Networks
Electronics 2019, 8(2), 194; https://doi.org/10.3390/electronics8020194
Received: 8 January 2019 / Revised: 26 January 2019 / Accepted: 29 January 2019 / Published: 7 February 2019
Cited by 2 | PDF Full-text (4626 KB) | HTML Full-text | XML Full-text
Abstract
This paper presents a QoS-aware, content-aware and device-aware nonintrusive medical QoE (m-QoE) prediction model over small cell networks. The proposed prediction model utilises a Multilayer Perceptron (MLP) neural network to predict m-QoE. It also acts as a platform to maintain and optimise the [...] Read more.
This paper presents a QoS-aware, content-aware and device-aware nonintrusive medical QoE (m-QoE) prediction model over small cell networks. The proposed prediction model utilises a Multilayer Perceptron (MLP) neural network to predict m-QoE. It also acts as a platform to maintain and optimise the acceptable diagnostic quality through a device-aware adaptive video streaming mechanism. The proposed model is trained for an unseen dataset of input variables such as QoS, content features and display device characteristics, to produce an output value in the form of m-QoE (i.e. MOS). The efficiency of the proposed model is validated through subjective tests carried by medical experts. The prediction accuracy obtained via the correlation coefficient and Root Mean-Square-Error (RMSE) indicates that the proposed model succeeds in measuring m-QoE closer to the visual perception of the medical experts. Furthermore, we have addressed two main research questions: (1) How significant is ultrasound video content type in determining m-QoE? (2) How much of a role does the screen size and device resolution play in medical experts’ diagnostic experience? The former is answered through the content classification of ultrasound video sequences based on their spatiotemporal features, by including these features in the proposed prediction model, and validating their significance through medical experts’ subjective ratings. The latter is answered by conducting a novel subjective experiment of the ultrasound video sequences across multiple devices. Full article
(This article belongs to the Special Issue Electronic Solutions for Artificial Intelligence Healthcare)
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Review

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Open AccessReview
A Review on the Role of Blockchain Technology in the Healthcare Domain
Electronics 2019, 8(6), 679; https://doi.org/10.3390/electronics8060679
Received: 31 March 2019 / Revised: 9 May 2019 / Accepted: 12 May 2019 / Published: 15 June 2019
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Abstract
Recently, there have been increasing calls for healthcare providers to provide controls for patients over their personal health records. Nevertheless, security issues concerning how different healthcare providers exchange healthcare information have caused a flop in the deployment of such systems. The ability to [...] Read more.
Recently, there have been increasing calls for healthcare providers to provide controls for patients over their personal health records. Nevertheless, security issues concerning how different healthcare providers exchange healthcare information have caused a flop in the deployment of such systems. The ability to exchange data securely is important so that new borderless integrated healthcare services can be provided to patients. Due to its decentralized nature, blockchain technology is a suitable driver for the much-needed shift towards integrated healthcare, providing new insights and addressing some of the main challenges of many healthcare areas. Blockchain allows healthcare providers to record and manage peer-to-peer transactions through a network without central authority. In this paper, we discuss the concept of blockchain technology and hurdles in their adoption in the healthcare domain. Furthermore, a review is conducted on the latest implementations of blockchain technology in healthcare. Finally, a new case study of a blockchain-based healthcare platform is presented addressing the drawbacks of current designs, followed by recommendations for future blockchain researchers and developers. Full article
(This article belongs to the Special Issue Electronic Solutions for Artificial Intelligence Healthcare)
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