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4 pages, 161 KiB

Open AccessEditorial

Electronic Solutions for Artificial Intelligence Healthcare

by

Hyeyoung Ko

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Jun-Ho Huh

Electronics 2021, 10(19), 2421; https://doi.org/10.3390/electronics10192421 - 04 Oct 2021

Cited by 4 | Viewed by 1962

Abstract

At present, diverse, innovative technology is used in electronics and ubiquitous computing environments [...] Full article

(This article belongs to the Special Issue Electronic Solutions for Artificial Intelligence Healthcare)

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Research

Jump to: Editorial, Review

17 pages, 5030 KiB

Open AccessFeature PaperArticle

Traffic Inference System Using Correlation Analysis with Various Predicted Big Data

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Yonghoon Kim

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Jun-Ho Huh

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Mokdong Chung

Electronics 2021, 10(3), 354; https://doi.org/10.3390/electronics10030354 - 02 Feb 2021

Cited by 3 | Viewed by 1784

Abstract

Currently, most of the transportation systems require changes to intelligent transportation systems, but most of them focus on efficient transportation rather than on improvement in human life. Sometimes, traffic systems are designed for economic value, and safety-related issues are neglected. A traffic information [...] Read more.

Currently, most of the transportation systems require changes to intelligent transportation systems, but most of them focus on efficient transportation rather than on improvement in human life. Sometimes, traffic systems are designed for economic value, and safety-related issues are neglected. A traffic information system that reflects various kinds of environmental information related to people’s safety must be able to reflect not only the existing economic goals but also a safe traffic environment. The traffic environment can be thought of as safety and direct information such as rainfall, including information on specific days when many people are scheduled to be gathered for certain events nearby. Intelligent transportation systems using this information can provide safety-related information for traveling to a specific area or for business trips. In addition, traffic congestion is a social problem and is directly related to a comfort life for individuals. Therefore, addressing various social and environmental factors could make human life more stable and reduce stress as a result. To do that, we need to estimate the impact on traffic based on environmental Big Data. The data can generally be divided into structured data and unstructured data. In inference, structured data analysis is relatively easy due to the precise meaning of the data. Nonetheless, it can be very difficult to predict environmentally sensitive data, such as traffic volume in intelligent transportation systems. To cope with this problem, there are a few systems for handling unstructured data to find out specific events that affect the traffic volume and improve its reliability. This paper shows that it is possible to estimate the exact volume of traffic using correlation analysis with various predicted data. Thus, we may apply this technique to the existing intelligent transportation system to predict the exact volume of traffic with environmentally sensitive data including various unstructured data. Full article

(This article belongs to the Special Issue Electronic Solutions for Artificial Intelligence Healthcare)

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21 pages, 1451 KiB

Open AccessArticle

Impact of Unreliable Content on Social Media Users during COVID-19 and Stance Detection System

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Mudasir Ahmad Wani

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Nancy Agarwal

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Patrick Bours

Electronics 2021, 10(1), 5; https://doi.org/10.3390/electronics10010005 - 23 Dec 2020

Cited by 21 | Viewed by 5008

Abstract

The abundant dissemination of misinformation regarding coronavirus disease 2019 (COVID-19) presents another unprecedented issue to the world, along with the health crisis. Online social network (OSN) platforms intensify this problem by allowing their users to easily distort and fabricate the information and disseminate [...] Read more.

The abundant dissemination of misinformation regarding coronavirus disease 2019 (COVID-19) presents another unprecedented issue to the world, along with the health crisis. Online social network (OSN) platforms intensify this problem by allowing their users to easily distort and fabricate the information and disseminate it farther and rapidly. In this paper, we study the impact of misinformation associated with a religious inflection on the psychology and behavior of the OSN users. The article presents a detailed study to understand the reaction of social media users when exposed to unverified content related to the Islamic community during the COVID-19 lockdown period in India. The analysis was carried out on Twitter users where the data were collected using three scraping packages, Tweepy, Selenium, and Beautiful Soup, to cover more users affected by this misinformation. A labeled dataset is prepared where each tweet is assigned one of the four reaction polarities, namely, E (endorse), D (deny), Q (question), and N (neutral). Analysis of collected data was carried out in five phases where we investigate the engagement of E, D, Q, and N users, tone of the tweets, and the consequence upon repeated exposure of such information. The evidence demonstrates that the circulation of such content during the pandemic and lockdown phase had made people more vulnerable in perceiving the unreliable tweets as fact. It was also observed that people absorbed the negativity of the online content, which induced a feeling of hatred, anger, distress, and fear among them. People with similar mindset form online groups and express their negative attitude to other groups based on their opinions, indicating the strong signals of social unrest and public tensions in society. The paper also presents a deep learning-based stance detection model as one of the automated mechanisms for tracking the news on Twitter as being potentially false. Stance classifier aims to predict the attitude of a tweet towards a news headline and thereby assists in determining the veracity of news by monitoring the distribution of different reactions of the users towards it. The proposed model, employing deep learning (convolutional neural network(CNN)) and sentence embedding (bidirectional encoder representations from transformers(BERT)) techniques, outperforms the existing systems. The performance is evaluated on the benchmark SemEval stance dataset. Furthermore, a newly annotated dataset is prepared and released with this study to help the research of this domain. Full article

(This article belongs to the Special Issue Electronic Solutions for Artificial Intelligence Healthcare)

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18 pages, 7373 KiB

Open AccessArticle

Development of a Portable Multi-Sensor Urine Test and Data Collection Platform for Risk Assessment of Kidney Stone Formation

by

Wen-Yaw Chung

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Roozbeh Falah Ramezani

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Angelito A. Silverio

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Vincent F. Tsai

Electronics 2020, 9(12), 2180; https://doi.org/10.3390/electronics9122180 - 18 Dec 2020

Cited by 4 | Viewed by 2879

Abstract

In this paper, we present an Internet of things (IoT)-based data collection system for the risk assessment of urinary stone formation, or urolithiasis, by the measurement and storage of four parameters in urine: pH, concentrations of ionized calcium (Ca²⁺), uric acid [...] Read more.

In this paper, we present an Internet of things (IoT)-based data collection system for the risk assessment of urinary stone formation, or urolithiasis, by the measurement and storage of four parameters in urine: pH, concentrations of ionized calcium (Ca²⁺), uric acid and total dissolved solids. The measurements collected by the system from patients and healthy individuals grouped by age and gender will be stored in a cloud database. These will be used in the training phase of an artificial intelligence (AI) machine learning process utilizing the logistics regression model. The trained model provides a binary risk assessment, indicating if the end user is either a stone-former or not. For system validation, standard chemical solutions were used. Preliminary results indicated a sufficient measurement range, falling within the physiological range, and resolution for pH (2.0–10.0, +/−0.1), Ca²⁺(0.1–3.0 mmol/l, +/−0.05), uric acid (20–500 ppm, +/−1) and conductivity (1.0–40.0 mS/cm, +/−0.1), exhibiting high correlation with standard instruments. We intend to deploy this system in few hospitals in Taiwan to collect the data of patients’ urine, with analysis aided by urologist assessments for the risk of urolithiasis. The modularized design allows future modification and expansion to accommodate other sensing analytes. Full article

(This article belongs to the Special Issue Electronic Solutions for Artificial Intelligence Healthcare)

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21 pages, 7572 KiB

Open AccessArticle

CNN-Based Network Intrusion Detection against Denial-of-Service Attacks

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Electronics 2020, 9(6), 916; https://doi.org/10.3390/electronics9060916 - 01 Jun 2020

Cited by 157 | Viewed by 11877

Abstract

As cyberattacks become more intelligent, it is challenging to detect advanced attacks in a variety of fields including industry, national defense, and healthcare. Traditional intrusion detection systems are no longer enough to detect these advanced attacks with unexpected patterns. Attackers bypass known signatures [...] Read more.

As cyberattacks become more intelligent, it is challenging to detect advanced attacks in a variety of fields including industry, national defense, and healthcare. Traditional intrusion detection systems are no longer enough to detect these advanced attacks with unexpected patterns. Attackers bypass known signatures and pretend to be normal users. Deep learning is an alternative to solving these issues. Deep Learning (DL)-based intrusion detection does not require a lot of attack signatures or the list of normal behaviors to generate detection rules. DL defines intrusion features by itself through training empirical data. We develop a DL-based intrusion model especially focusing on denial of service (DoS) attacks. For the intrusion dataset, we use KDD CUP 1999 dataset (KDD), the most widely used dataset for the evaluation of intrusion detection systems (IDS). KDD consists of four types of attack categories, such as DoS, user to root (U2R), remote to local (R2L), and probing. Numerous KDD studies have been employing machine learning and classifying the dataset into the four categories or into two categories such as attack and benign. Rather than focusing on the broad categories, we focus on various attacks belonging to same category. Unlike other categories of KDD, the DoS category has enough samples for training each attack. In addition to KDD, we use CSE-CIC-IDS2018 which is the most up-to-date IDS dataset. CSE-CIC-IDS2018 consists of more advanced DoS attacks than that of KDD. In this work, we focus on the DoS category of both datasets and develop a DL model for DoS detection. We develop our model based on a Convolutional Neural Network (CNN) and evaluate its performance through comparison with an Recurrent Neural Network (RNN). Furthermore, we suggest the optimal CNN design for the better performance through numerous experiments. Full article

(This article belongs to the Special Issue Electronic Solutions for Artificial Intelligence Healthcare)

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21 pages, 3680 KiB

Open AccessArticle

Surgery Agreement Signature Authentication System for Mobile Health Care

by

Jun-Ho Huh

Electronics 2020, 9(6), 890; https://doi.org/10.3390/electronics9060890 - 27 May 2020

Cited by 5 | Viewed by 2377

Abstract

Currently, the use of biometric systems is increasing following the increase in the non-face-to-face security transactions in the Health Care sector, where smart devices are extensively used. Additionally, hospital patients or their guardians had to sign every medical/surgery consent form with a pen. [...] Read more.

Currently, the use of biometric systems is increasing following the increase in the non-face-to-face security transactions in the Health Care sector, where smart devices are extensively used. Additionally, hospital patients or their guardians had to sign every medical/surgery consent form with a pen. Currently, hospitals are attempting to digitalize the form to avoid its loss or delay to the operating room. Thus, this study proposes a surgery consent signature authentication system for the mobile health care system. Along with the vein or the fingerprint recognition technology, the smart electronic signature recognition technology is regarded as a new type of security solution for Mobile Health Care, which is a compound of Health Care and technology, or a smart contents and display technology. Thus, this study proposes a surgery agreement signature authentication system for Mobile Health Care while using the techniques, such as database segment units comparison in the cloud, Bag of Word, etc. The proposed system was implemented with Java language and developed in a way the reference signature stored in advance in a cloud database to be compared with the signature currently entered. For the comparison, the segment matching, spatial pyramid matching, and boundary matching techniques were used in addition to the Dynamic Time Warping (DTW) algorithm. Additionally, the system has been made lighter than the existing experimental products, so that it is easier to embed the system into a smart phone, tablet, or others. The Test Bed experiment result showed that the system operated flexibly. Full article

(This article belongs to the Special Issue Electronic Solutions for Artificial Intelligence Healthcare)

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17 pages, 4497 KiB

Open AccessEditor’s ChoiceArticle

Closing the Wearable Gap—Part VI: Human Gait Recognition Using Deep Learning Methodologies

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Electronics 2020, 9(5), 796; https://doi.org/10.3390/electronics9050796 - 12 May 2020

Cited by 17 | Viewed by 3758

Abstract

A novel wearable solution using soft robotic sensors (SRS) has been investigated to model foot-ankle kinematics during gait cycles. The capacitance of SRS related to foot-ankle basic movements was quantified during the gait movements of 20 participants on a flat surface as well [...] Read more.

A novel wearable solution using soft robotic sensors (SRS) has been investigated to model foot-ankle kinematics during gait cycles. The capacitance of SRS related to foot-ankle basic movements was quantified during the gait movements of 20 participants on a flat surface as well as a cross-sloped surface. In order to evaluate the power of SRS in modeling foot-ankle kinematics, three-dimensional (3D) motion capture data was also collected for analyzing gait movement. Three different approaches were employed to quantify the relationship between the SRS and the 3D motion capture system, including multivariable linear regression, an artificial neural network (ANN), and a time-series long short-term memory (LSTM) network. Models were compared based on the root mean squared error (RMSE) of the prediction of the joint angle of the foot in the sagittal and frontal plane, collected from the motion capture system. There was not a significant difference between the error rates of the three different models. The ANN resulted in an average RMSE of 3.63, being slightly more successful in comparison to the average RMSE values of 3.94 and 3.98 resulting from multivariable linear regression and LSTM, respectively. The low error rate of the models revealed the high performance of SRS in capturing foot-ankle kinematics during the human gait cycle. Full article

(This article belongs to the Special Issue Electronic Solutions for Artificial Intelligence Healthcare)

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24 pages, 5648 KiB

Open AccessArticle

Intelligent Image Synthesis for Accurate Retinal Diagnosis

by

Dong-Gun Lee

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Yonghun Jang

and

Yeong-Seok Seo

Electronics 2020, 9(5), 767; https://doi.org/10.3390/electronics9050767 - 07 May 2020

Cited by 4 | Viewed by 2536

Abstract

Ophthalmology is a core medical field that is of interest to many. Retinal examination is a commonly performed diagnostic procedure that can be used to inspect the interior of the eye and screen for any pathological symptoms. Although various types of eye examinations [...] Read more.

Ophthalmology is a core medical field that is of interest to many. Retinal examination is a commonly performed diagnostic procedure that can be used to inspect the interior of the eye and screen for any pathological symptoms. Although various types of eye examinations exist, there are many cases where it is difficult to identify the retinal condition of the patient accurately because the test image resolution is very low because of the utilization of simple methods. In this paper, we propose an image synthetic approach that reconstructs the vessel image based on past retinal image data using the multilayer perceptron concept with artificial neural networks. The approach proposed in this study can convert vessel images to vessel-centered images with clearer identification, even for low-resolution retinal images. To verify the proposed approach, we determined whether high-resolution vessel images could be extracted from low-resolution images through a statistical analysis using high- and low-resolution images extracted from the same patient. Full article

(This article belongs to the Special Issue Electronic Solutions for Artificial Intelligence Healthcare)

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18 pages, 7051 KiB

Open AccessArticle

An Energy Efficient Enhanced Dual-Fuzzy Logic Routing Protocol for Monitoring Activities of the Elderly Using Body Sensor Networks

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Electronics 2020, 9(5), 723; https://doi.org/10.3390/electronics9050723 - 28 Apr 2020

Cited by 9 | Viewed by 2438

Abstract

Wireless body area networks (WBANs) are an important application in wireless sensor networks (WSNs). Specifically, in healthcare monitoring systems, it is important to screen the patient’s biometric signals. For example, the elderlies’ vital signs, such as ECG (Electrocardiogram), blood pressure, heart rate, and [...] Read more.

Wireless body area networks (WBANs) are an important application in wireless sensor networks (WSNs). Specifically, in healthcare monitoring systems, it is important to screen the patient’s biometric signals. For example, the elderlies’ vital signs, such as ECG (Electrocardiogram), blood pressure, heart rate, and blood glucose, can be used as measures of their well-being and are all critically important for remote elderly care in tracking their physical and cognitive capabilities. Therefore, WBANs require higher energy efficiency and data transmission. This paper proposes a cluster-based routing protocol which is suitable for WBANs while analyzing energy efficiency issue in data transmission. Considering the importance of sensor nodes in a specific environment for improving the network’s lifetime, the protocol based on the LEACH (low energy adaptive clustering hierarchy) algorithm is proposed. Due to its avoidance of long-distance transmission, the clustering technique is an efficient algorithm for prolonging the lifetimes of sensor networks. Therefore, this paper suggests an enhanced LEACH-dual fuzzy logic (ELEACH-DFL) protocol based-on clustering for CH (cluster head) selection and cluster configuration in wireless sensor networks. The simulation and analysis results address that the enhanced algorithm reduces the energy consumption effectively and extends the lifespan of the entire network. For wired sensors, attaching sensors to the user may cause problems and inconvenience of mobility. This leads to the use of wireless sensors to proceed with body sensors, which should consider the problem of battery efficiency, which concerns the configuration of wireless sensors. The LEACH protocol is energy efficient until the first node dead is generated. However, there is a sharp drop in energy efficiency after that. The ELEACH-DFL protocol has the advantage of maintaining energy efficiency even after the first node dead is generated, with the utmost consideration being given to stability in consideration of cluster selection and cluster head selection. In a field of 50 × 50, the FND efficiency improvement rate of ELEACH-DFL versus LEACH protocol is approximately 32%. In addition, in a field of 50 × 150, the FND efficiency improvement rate of ELEACH-DFL versus LEACH protocol is approximately 159%. Full article

(This article belongs to the Special Issue Electronic Solutions for Artificial Intelligence Healthcare)

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31 pages, 14018 KiB

Open AccessArticle

BEGAN v3: Avoiding Mode Collapse in GANs Using Variational Inference

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Sung-Wook Park

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Jun-Ho Huh

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Jong-Chan Kim

Electronics 2020, 9(4), 688; https://doi.org/10.3390/electronics9040688 - 23 Apr 2020

Cited by 19 | Viewed by 4966

Abstract

In the field of deep learning, the generative model did not attract much attention until GANs (generative adversarial networks) appeared. In 2014, Google’s Ian Goodfellow proposed a generative model called GANs. GANs use different structures and objective functions from the existing generative model. [...] Read more.

In the field of deep learning, the generative model did not attract much attention until GANs (generative adversarial networks) appeared. In 2014, Google’s Ian Goodfellow proposed a generative model called GANs. GANs use different structures and objective functions from the existing generative model. For example, GANs use two neural networks: a generator that creates a realistic image, and a discriminator that distinguishes whether the input is real or synthetic. If there are no problems in the training process, GANs can generate images that are difficult even for experts to distinguish in terms of authenticity. Currently, GANs are the most researched subject in the field of computer vision, which deals with the technology of image style translation, synthesis, and generation, and various models have been unveiled. The issues raised are also improving one by one. In image synthesis, BEGAN (Boundary Equilibrium Generative Adversarial Network), which outperforms the previously announced GANs, learns the latent space of the image, while balancing the generator and discriminator. Nonetheless, BEGAN also has a mode collapse wherein the generator generates only a few images or a single one. Although BEGAN-CS (Boundary Equilibrium Generative Adversarial Network with Constrained Space), which was improved in terms of loss function, was introduced, it did not solve the mode collapse. The discriminator structure of BEGAN-CS is AE (AutoEncoder), which cannot create a particularly useful or structured latent space. Compression performance is not good either. In this paper, this characteristic of AE is considered to be related to the occurrence of mode collapse. Thus, we used VAE (Variational AutoEncoder), which added statistical techniques to AE. As a result of the experiment, the proposed model did not cause mode collapse but converged to a better state than BEGAN-CS. Full article

(This article belongs to the Special Issue Electronic Solutions for Artificial Intelligence Healthcare)

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18 pages, 2438 KiB

Open AccessArticle

Deep Learning Models for Classification of Red Blood Cells in Microscopy Images to Aid in Sickle Cell Anemia Diagnosis

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Electronics 2020, 9(3), 427; https://doi.org/10.3390/electronics9030427 - 04 Mar 2020

Cited by 84 | Viewed by 16840

Abstract

Sickle cell anemia, which is also called sickle cell disease (SCD), is a hematological disorder that causes occlusion in blood vessels, leading to hurtful episodes and even death. The key function of red blood cells (erythrocytes) is to supply all the parts of [...] Read more.

Sickle cell anemia, which is also called sickle cell disease (SCD), is a hematological disorder that causes occlusion in blood vessels, leading to hurtful episodes and even death. The key function of red blood cells (erythrocytes) is to supply all the parts of the human body with oxygen. Red blood cells (RBCs) form a crescent or sickle shape when sickle cell anemia affects them. This abnormal shape makes it difficult for sickle cells to move through the bloodstream, hence decreasing the oxygen flow. The precise classification of RBCs is the first step toward accurate diagnosis, which aids in evaluating the danger level of sickle cell anemia. The manual classification methods of erythrocytes require immense time, and it is possible that errors may be made throughout the classification stage. Traditional computer-aided techniques, which have been employed for erythrocyte classification, are based on handcrafted features techniques, and their performance relies on the selected features. They also are very sensitive to different sizes, colors, and complex shapes. However, microscopy images of erythrocytes are very complex in shape with different sizes. To this end, this research proposes lightweight deep learning models that classify the erythrocytes into three classes: circular (normal), elongated (sickle cells), and other blood content. These models are different in the number of layers and learnable filters. The available datasets of red blood cells with sickle cell disease are very small for training deep learning models. Therefore, addressing the lack of training data is the main aim of this paper. To tackle this issue and optimize the performance, the transfer learning technique is utilized. Transfer learning does not significantly affect performance on medical image tasks when the source domain is completely different from the target domain. In some cases, it can degrade the performance. Hence, we have applied the same domain transfer learning, unlike other methods that used the ImageNet dataset for transfer learning. To minimize the overfitting effect, we have utilized several data augmentation techniques. Our model obtained state-of-the-art performance and outperformed the latest methods by achieving an accuracy of 99.54% with our model and 99.98% with our model plus a multiclass SVM classifier on the erythrocytesIDB dataset and 98.87% on the collected dataset. Full article

(This article belongs to the Special Issue Electronic Solutions for Artificial Intelligence Healthcare)

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15 pages, 5244 KiB

Open AccessArticle

StoolNet for Color Classification of Stool Medical Images

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Ziyuan Yang

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Lu Leng

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Byung-Gyu Kim

Electronics 2019, 8(12), 1464; https://doi.org/10.3390/electronics8121464 - 02 Dec 2019

Cited by 31 | Viewed by 4908

Abstract

The color classification of stool medical images is commonly used to diagnose digestive system diseases, so it is important in clinical examination. In order to reduce laboratorians’ heavy burden, advanced digital image processing technologies and deep learning methods are employed for the automatic [...] Read more.

The color classification of stool medical images is commonly used to diagnose digestive system diseases, so it is important in clinical examination. In order to reduce laboratorians’ heavy burden, advanced digital image processing technologies and deep learning methods are employed for the automatic color classification of stool images in this paper. The region of interest (ROI) is segmented automatically and then classified with a shallow convolutional neural network (CNN) dubbed StoolNet. Thanks to its shallow structure and accurate segmentation, StoolNet can converge quickly. The sufficient experiments confirm the good performance of StoolNet and the impact of the different training sample numbers on StoolNet. The proposed method has several advantages, such as low cost, accurate automatic segmentation, and color classification. Therefore, it can be widely used in artificial intelligence (AI) healthcare. Full article

(This article belongs to the Special Issue Electronic Solutions for Artificial Intelligence Healthcare)

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20 pages, 2685 KiB

Open AccessArticle

Fake News Analysis Modeling Using Quote Retweet

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Yonghun Jang

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Chang-Hyeon Park

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Yeong-Seok Seo

Electronics 2019, 8(12), 1377; https://doi.org/10.3390/electronics8121377 - 20 Nov 2019

Cited by 26 | Viewed by 12444

Abstract

Fake news can confuse many people in the area of politics, culture, healthcare, etc. Fake news refers to news containing misleading or fabricated contents that are actually groundless; they are intentionally exaggerated or provide false information. As such, fake news can distort reality [...] Read more.

Fake news can confuse many people in the area of politics, culture, healthcare, etc. Fake news refers to news containing misleading or fabricated contents that are actually groundless; they are intentionally exaggerated or provide false information. As such, fake news can distort reality and cause social problems, such as self-misdiagnosis of medical issues. Many academic researchers have been collecting data from social and medical media, which are sources of various information flows, and conducting studies to analyse and detect fake news. However, in the case of conventional studies, the features used for analysis are limited, and the consideration for newly added features of social media is lacking. Therefore, this study proposes a fake news analysis modelling method by identifying a variety of features and collecting various data from Twitter, a social media outlet with a good deal of power in terms of spreading information. The method proposed in this study can increase the accuracy of fake news analysis by acquiring more potential information from the Quote Retweet feature added to Twitter in 2015, compared to the more conventional and common Retweet only. Furthermore, fake news was analysed through neural network-based classification modelling by using the preprocessed data and the identified best features in the learning data. In the performance results, using the neural network-based classifier, the classification model that also used Quote Retweet, showed an improvement in performance over the conventional methods, and it was confirmed that the identified best features had a significant impact on increasing the classification accuracy of fake news. Full article

(This article belongs to the Special Issue Electronic Solutions for Artificial Intelligence Healthcare)

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19 pages, 4814 KiB

Open AccessFeature PaperArticle

A Prototype Photoplethysmography Electronic Device that Distinguishes Congestive Heart Failure from Healthy Individuals by Applying Natural Time Analysis

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George Baldoumas

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Dimitrios Peschos

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Giorgos Tatsis

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Spyridon K. Chronopoulos

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Electronics 2019, 8(11), 1288; https://doi.org/10.3390/electronics8111288 - 05 Nov 2019

Cited by 29 | Viewed by 4127

Abstract

In this paper, a prototype photoplethysmography (PPG) electronic device is presented for the distinction of individuals with congestive heart failure (CHF) from the healthy (H) by applying the concept of Natural Time Analysis (NTA). Data were collected simultaneously with a conventional three-electrode electrocardiography [...] Read more.

In this paper, a prototype photoplethysmography (PPG) electronic device is presented for the distinction of individuals with congestive heart failure (CHF) from the healthy (H) by applying the concept of Natural Time Analysis (NTA). Data were collected simultaneously with a conventional three-electrode electrocardiography (ECG) system and our prototype PPG electronic device from H and CHF volunteers at the 2nd Department of Cardiology, Medical School of Ioannina, Greece. Statistical analysis of the results show a clear separation of CHF from H subjects by means of NTA for both the conventional ECG system and our PPG prototype system, with a clearly better distinction for the second one which additionally inherits the advantages of a low-cost portable device. Full article

(This article belongs to the Special Issue Electronic Solutions for Artificial Intelligence Healthcare)

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15 pages, 3087 KiB

Open AccessArticle

Melody Extraction and Encoding Method for Generating Healthcare Music Automatically

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Shuyu Li

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Sejun Jang

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Yunsick Sung

Electronics 2019, 8(11), 1250; https://doi.org/10.3390/electronics8111250 - 31 Oct 2019

Cited by 7 | Viewed by 4384

Abstract

The strong relationship between music and health has helped prove that soft and peaceful classical music can significantly reduce people’s stress; however, it is difficult to identify and collect examples of such music to build a library. Therefore, a system is required that [...] Read more.

The strong relationship between music and health has helped prove that soft and peaceful classical music can significantly reduce people’s stress; however, it is difficult to identify and collect examples of such music to build a library. Therefore, a system is required that can automatically generate similar classical music selections from a small amount of input music. Melody is the main element that reflects the rhythms and emotions of musical works; therefore, most automatic music generation research is based on melody. Given that melody varies frequently within musical bars, the latter are used as the basic units of composition. As such, there is a requirement for melody extraction techniques and bar-based encoding methods for automatic generation of bar-based music using melodies. This paper proposes a method that handles melody track extraction and bar encoding. First, the melody track is extracted using a pitch-based term frequency–inverse document frequency (TFIDF) algorithm and a feature-based filter. Subsequently, four specific features of the notes within a bar are encoded into a fixed-size matrix during bar encoding. We conduct experiments to determine the accuracy of track extraction based on verification data obtained with the TFIDF algorithm and the filter; an accuracy of 94.7% was calculated based on whether the extracted track was a melody track. The estimated value demonstrates that the proposed method can accurately extract melody tracks. This paper discusses methods for automatically extracting melody tracks from MIDI files and encoding based on bars. The possibility of generating music through deep learning neural networks is facilitated by the methods we examine within this work. To help the neural networks generate higher quality music, which is good for human health, the data preprocessing methods contained herein should be improved in future works. Full article

(This article belongs to the Special Issue Electronic Solutions for Artificial Intelligence Healthcare)

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17 pages, 3145 KiB

Open AccessFeature PaperArticle

A Mechanism of Masking Identification Information regarding Moving Objects Recorded on Visual Surveillance Systems by Differentially Implementing Access Permission

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Namje Park

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Byung-Gyu Kim

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Jinsu Kim

Electronics 2019, 8(7), 735; https://doi.org/10.3390/electronics8070735 - 28 Jun 2019

Cited by 21 | Viewed by 3523

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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14 pages, 6706 KiB

Open AccessArticle

Improved Heart-Rate Measurement from Mobile Face Videos

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Jean-Pierre Lomaliza

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Hanhoon Park

Electronics 2019, 8(6), 663; https://doi.org/10.3390/electronics8060663 - 12 Jun 2019

Cited by 14 | Viewed by 4375

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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27 pages, 2520 KiB

Open AccessArticle

Indoor Positioning System: A New Approach Based on LSTM and Two Stage Activity Classification

by

Ghulam Hussain

,

Muhammad Shahid Jabbar

,

Jun-Dong Cho

and

Sangmin Bae

Electronics 2019, 8(4), 375; https://doi.org/10.3390/electronics8040375 - 28 Mar 2019

Cited by 28 | Viewed by 4200

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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27 pages, 4626 KiB

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

by

Ikram U. Rehman

,

Moustafa M. Nasralla

and

Nada Y. Philip

Electronics 2019, 8(2), 194; https://doi.org/10.3390/electronics8020194 - 07 Feb 2019

Cited by 16 | Viewed by 3929

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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12 pages, 792 KiB

Open AccessReview

Analysis of the Results of Heel-Rise Test with Sensors: A Systematic Review

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,

and

Electronics 2020, 9(7), 1154; https://doi.org/10.3390/electronics9071154 - 17 Jul 2020

Cited by 10 | Viewed by 2335

Abstract

Strokes are a constant concern for people and pose a major health concern. Tests that allow detection and the rehabilitation of patients have started to become more important and essential. There are several tests used by physiotherapists to speed up the recovery process [...] Read more.

Strokes are a constant concern for people and pose a major health concern. Tests that allow detection and the rehabilitation of patients have started to become more important and essential. There are several tests used by physiotherapists to speed up the recovery process of patients. This article presents a systematic review of existing studies using the Heel-Rise Test and sensors (i.e., accelerometers, gyroscopes, pressure and tilt sensors) to estimate the different levels and health statuses of individuals. It was found that the most measured parameter was related to the number of repetitions, and the maximum number of repetitions for a healthy adult is 25 repetitions. As for future work, the implementation of these methods with a simple mobile device will facilitate the different measurements on this subject. Full article

(This article belongs to the Special Issue Electronic Solutions for Artificial Intelligence Healthcare)

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17 pages, 807 KiB

Open AccessReview

Measurement of Results of Functional Reach Test with Sensors: A Systematic Review

by

Ivan Miguel Pires

,

Nuno M. Garcia

and

Eftim Zdravevski

Electronics 2020, 9(7), 1078; https://doi.org/10.3390/electronics9071078 - 30 Jun 2020

Cited by 8 | Viewed by 8559

Abstract

The test of physical conditions is important to treat and presents several diseases related to the movement. These diseases are mainly related to the physiotherapy and orthopedy, but it can be applied in a wide range of medical specialties. The Functional Reach Test [...] Read more.

The test of physical conditions is important to treat and presents several diseases related to the movement. These diseases are mainly related to the physiotherapy and orthopedy, but it can be applied in a wide range of medical specialties. The Functional Reach Test is one of the most common physical tests used to measure the limit of stability that is highly important for older adults because their stability is reduced with aging. Thus, older adults are part of the population more exposed to stroke. This test may help in the measurement of the conditions related to post-stroke and stroke treatment. The movements related to this test may be recorded and recognized with the inertial sensors available in off-the-shelf mobile devices. This systematic review aims to determine how to determine the conditions related to this test, which can be detected, and which of the sensors are used for this purpose. The main contribution of this paper is to present the research on the state-of-the-art use of sensors available on off-the-shelf mobile devices to measure Functional Reach Test results. This research shows that the sensors that are used in the literature studies are inertial sensors and force sensors. The features extracted from the different studies are categorized as dynamic balance, quantitative, and raw statistics. These features are mainly used to recognize the different parameters of the test, and several accidents, including falling. The execution of this test may allow the early detection of different diseases. Full article

(This article belongs to the Special Issue Electronic Solutions for Artificial Intelligence Healthcare)

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22 pages, 5196 KiB

Open AccessFeature PaperReview

Overview of Maximum Power Point Tracking Methods for PV System in Micro Grid

by

Jae-Sub Ko

,

Jun-Ho Huh

and

Jong-Chan Kim

Electronics 2020, 9(5), 816; https://doi.org/10.3390/electronics9050816 - 15 May 2020

Cited by 41 | Viewed by 7929

Abstract

This paper presents an overview of the maximum power point tracking (MPPT) methods for photovoltaic (PV) systems used in the Micro Grids of PV systems. In the PV system, the output varies nonlinearly with temperature and radiation, and the point at which power [...] Read more.

This paper presents an overview of the maximum power point tracking (MPPT) methods for photovoltaic (PV) systems used in the Micro Grids of PV systems. In the PV system, the output varies nonlinearly with temperature and radiation, and the point at which power is maximized appears accordingly. The MPPT of the PV system can improve output by about 25%, and it is very important to operate at this point at all times. Various methods of tracking the MPP of the PV system have been studied and proposed. In this paper, we discuss commonly used methods for the MPPT of PV systems, methods using artificial intelligence control, and mixed methods, and present the characteristics, advantages, and disadvantages of each method. Full article

(This article belongs to the Special Issue Electronic Solutions for Artificial Intelligence Healthcare)

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16 pages, 1425 KiB

Open AccessReview

Identification of Diseases Based on the Use of Inertial Sensors: A Systematic Review

by

Vasco Ponciano

,

Ivan Miguel Pires

,

Fernando Reinaldo Ribeiro

,

Gonçalo Marques

,

Maria Vanessa Villasana

,

Nuno M. Garcia

,

Eftim Zdravevski

and

Susanna Spinsante

Electronics 2020, 9(5), 778; https://doi.org/10.3390/electronics9050778 - 08 May 2020

Cited by 10 | Viewed by 2915

Abstract

Inertial sensors are commonly embedded in several devices, including smartphones, and other specific devices. This type of sensors may be used for different purposes, including the recognition of different diseases. Several studies are focused on the use of accelerometer signals for the automatic [...] Read more.

Inertial sensors are commonly embedded in several devices, including smartphones, and other specific devices. This type of sensors may be used for different purposes, including the recognition of different diseases. Several studies are focused on the use of accelerometer signals for the automatic recognition of different diseases, and it may empower the different treatments with the use of less invasive and painful techniques for patients. This paper aims to provide a systematic review of the studies available in the literature for the automatic recognition of different diseases by exploiting accelerometer sensors. The most reliably detectable disease using accelerometer sensors, available in 54% of the analyzed studies, is the Parkinson’s disease. The machine learning methods implemented for the automatic recognition of Parkinson’s disease reported an accuracy of 94%. The recognition of other diseases is investigated in a few other papers, and it appears to be the target of further analysis in the future. Full article

(This article belongs to the Special Issue Electronic Solutions for Artificial Intelligence Healthcare)

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16 pages, 1486 KiB

Open AccessReview

Revisit of Password-Authenticated Key Exchange Protocol for Healthcare Support Wireless Communication

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,

and

Electronics 2020, 9(5), 733; https://doi.org/10.3390/electronics9050733 - 29 Apr 2020

Cited by 9 | Viewed by 2289

Abstract

Wireless communication is essential for the infrastructure of a healthcare system. This bidirectional communication is used for data collection and to control message delivery. Wireless communication is applied in industries as well as in our daily lives, e.g., smart cities; however, highly reliable [...] Read more.

Wireless communication is essential for the infrastructure of a healthcare system. This bidirectional communication is used for data collection and to control message delivery. Wireless communication is applied in industries as well as in our daily lives, e.g., smart cities; however, highly reliable communication may be more difficult in environments with low power consumption, many interferences, or IoT wireless network issues due to resource limitations. In order to solve these problems, we investigated the existing three-party password-authenticated key exchange (3PAKE) and developed an enhanced protocol. Currently, Lu et al. presented a 3PAKE protocol to improve the security flaws found in Farash and Attari’s protocol. This work revisits the protocol proposed by Lu et al. and demonstrates that, in addition to other security weaknesses, the protocol does not provide user anonymity which is an important issue for healthcare environment, and is not secure against insider attacks that may cause impersonation attacks. We propose a secure biometric-based efficient password-authenticated key exchange (SBAKE) protocol in order to remove the incidences of these threats, and present an analysis regarding the security and efficiency of the SBAKE protocol for practical deployment. Full article

(This article belongs to the Special Issue Electronic Solutions for Artificial Intelligence Healthcare)

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21 pages, 1076 KiB

Open AccessReview

Is The Timed-Up and Go Test Feasible in Mobile Devices? A Systematic Review

by

Vasco Ponciano

,

Ivan Miguel Pires

,

Fernando Reinaldo Ribeiro

,

Gonçalo Marques

,

,

,

and

Electronics 2020, 9(3), 528; https://doi.org/10.3390/electronics9030528 - 23 Mar 2020

Cited by 15 | Viewed by 9269

Abstract

The number of older adults is increasing worldwide, and it is expected that by 2050 over 2 billion individuals will be more than 60 years old. Older adults are exposed to numerous pathological problems such as Parkinson’s disease, amyotrophic lateral sclerosis, post-stroke, and [...] Read more.

The number of older adults is increasing worldwide, and it is expected that by 2050 over 2 billion individuals will be more than 60 years old. Older adults are exposed to numerous pathological problems such as Parkinson’s disease, amyotrophic lateral sclerosis, post-stroke, and orthopedic disturbances. Several physiotherapy methods that involve measurement of movements, such as the Timed-Up and Go test, can be done to support efficient and effective evaluation of pathological symptoms and promotion of health and well-being. In this systematic review, the authors aim to determine how the inertial sensors embedded in mobile devices are employed for the measurement of the different parameters involved in the Timed-Up and Go test. The main contribution of this paper consists of the identification of the different studies that utilize the sensors available in mobile devices for the measurement of the results of the Timed-Up and Go test. The results show that mobile devices embedded motion sensors can be used for these types of studies and the most commonly used sensors are the magnetometer, accelerometer, and gyroscope available in off-the-shelf smartphones. The features analyzed in this paper are categorized as quantitative, quantitative + statistic, dynamic balance, gait properties, state transitions, and raw statistics. These features utilize the accelerometer and gyroscope sensors and facilitate recognition of daily activities, accidents such as falling, some diseases, as well as the measurement of the subject’s performance during the test execution. Full article

(This article belongs to the Special Issue Electronic Solutions for Artificial Intelligence Healthcare)

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29 pages, 14838 KiB

Open AccessReview

A Review on the Role of Blockchain Technology in the Healthcare Domain

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and

Electronics 2019, 8(6), 679; https://doi.org/10.3390/electronics8060679 - 15 Jun 2019

Cited by 58 | Viewed by 11319

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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