• PNF and Movement
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• v.18 no.3
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• pp.365-373
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• 2020

Purpose: The COVID-19 pandemic has changed how healthcare is delivered worldwide and has affected the environment of the healthcare. Physical therapy in traditional healthcare systems can be difficult in unusual circumstances, such as the COVID-19 pandemic. Tele-physical therapy, defined as "the delivery of the physical therapy at a distance using electronic information and telecommunication technologies," will be a solution for this healthcare crisis. Thus, in this study, we proposed a mobile application for tele-physical therapy. Methods: This study used the Chrome Browser version 83.0.4 based on the Windows 10 64Bit operating system to use the App Inventor. To operate the mobile application, we used the Samsung Galaxy Note 9. The design of the mobile application was based on the review of a system used in the physical therapy department. Results: The graphical user interface (GUI) of the mobile application was displayed on three screens: selecting a painful joint (1st screen of the GUI); selecting a painful movement of the joint (2nd screen of the GUI); a self-manual therapy method and movie (3rd screen of the GUI). The proposed mobile application showed the stable repeatability of the self-manual therapy movie. Conclusion: The results of this study demonstrated that the proposed mobile application using the App Inventor for android will be able to create easy to use and reliable tele-physical therapy.

• Convergence Security Journal
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• v.7 no.1
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• pp.35-43
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• 2007

The object of this paper is to design a biomedical signal measure systems based on sensor networks for the patient, integrated with computing technology. Using a combination of zigbee RF, embedded hardware and software technologies, as it allows the healthcare center to receive the information on emergency situations and the ordinary state of the patients individually or simultaneously, the healthcare center can copy with quickly a state of emergency and assist the normal life of the patient. In order to meet the low power and other requirements for the proposed system, we introduce a zigbee based RP which is the most suitable solution for improving the performance of our beeper system.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.4
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• pp.260-264
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• 2009

There have been many studies on u-Healthcare system for human using sensor network systems. In this paper, we design and implement a healthcare system for pets called uPetCare(Ubiquitous Pet-Care System) that can manage the status of pet on the web. The main functions of this system are 1) gathering data using sensor network, 2) multi-hop communication in sensor network, 3) data compression and aggregation at sink node, 4) storing data in web server, 5) real-time data monitoring using AJAX, 6) activity recognition of pet.

• Journal of Information Processing Systems
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• v.15 no.6
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• pp.1462-1471
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• 2019

Real-time embedded systems have become pervasive in general industry. They also began to be applied in such domains as avionics, automotive, aerospace, healthcare, and industrial Internet. However, the system failure of such domains could result in catastrophic consequences. Runtime software testing is required in such domains that demands very high accuracy. Traditional runtime software testing based on handwork is very inefficient and time consuming. Hence, test automation methodologies in runtime is demanding. In this paper, we introduce a software testing system that translates a real-time software into a monitorable real-time software. The monitorable real-time software means the software provides the monitoring information in runtime. The monitoring target are time constraints of the input real-time software. We anticipate that our system lessens the burden of runtime software testing.

• Journal of Information Processing Systems
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• v.15 no.6
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• pp.1259-1264
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• 2019

Nowadays, cloud computing and big data analytics are at the center of many industries' concerns to take advantage of the potential benefits of building future smart cities. The integration of cloud computing and big data analytics is the main reason for massive adoption in many organizations, avoiding the potential complexities of on-premise big data systems. With these two technologies, the manufacturing industry, healthcare system, education, academe, etc. are developing rapidly, and they will offer various benefits to expand their domains. In this issue, we present a summary of 18 high-quality accepted articles following a rigorous review process in the field of cloud computing and big data analytics.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.9
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• pp.2512-2545
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• 2024

One of the biggest challenges for the medical professionals is spotting cardiovascular issues in the earliest stages. Around the world, Cardiovascular Diseases (CVD) are a major cause of death for almost 18 million people each year. Heart disease is therefore a serious concern that needs to be treated. The numerous elements that affect health, such as excessive blood pressure, elevated cholesterol, aberrant pulse rate, and many other factors, might make it challenging to detect heart disease. Consequently, early disease detection and the development of effective treatments can benefit greatly from the field of artificial intelligence. The purpose of this work is to develop a new IoT based healthcare monitoring framework for the prediction of CVD using machine learning algorithm. Here, the data preprocessing has been performed to create the normalized dataset for improving classification. Then, an Artificial Gorilla Troop Optimization (AGTO) algorithm is deployed to choose the most pertinent features from the normalized dataset. Moreover, the Multi-Linear Regression Classification (MLRC) model is also implemented for accurately categorizing the medical information as whether healthy or CVD affected. The results of the proposed AGTO-MLRC mechanism is validated and compared using the popular benchmarking datasets.

• Journal of Korea Multimedia Society
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• v.22 no.5
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• pp.588-600
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• 2019

Each year Malaria affects over 200 million people worldwide. Particularly, African continent is highly hit by this disease. According to many researches, this continent is ideal for Anopheles mosquitoes which transmit Malaria parasites to thrive. Rainfall volume is one of the major factor favoring the development of these Anopheles in the tropical Sub-Sahara Africa (SSA). However, the surveillance, monitoring and reporting of this epidemic is still poor and bureaucratic only. In our paper, we proposed a method to fast monitor and report Malaria instances by using Social Network Systems (SNS) and precipitation volume in Nigeria. We used Twitter search Application Programming Interface (API) to live-stream Twitter messages mentioning Malaria, preprocessed those Tweets and classified them into Malaria cases in Nigeria by using Support Vector Machine (SVM) classification algorithm and compared those Malaria cases with average precipitation volume. The comparison yielded a correlation of 0.75 between Malaria cases recorded by using Twitter and average precipitations in Nigeria. To ensure the certainty of our classification algorithm, we used an oversampling technique and eliminated the imbalance in our training Tweets.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.5
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• pp.1856-1869
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• 2015

This paper addresses the issues of 3D human activity detection, tracking and recognition from RGB-D video sequences using a feature structured framework. During human tracking and activity recognition, initially, dense depth images are captured using depth camera. In order to track human silhouettes, we considered spatial/temporal continuity, constraints of human motion information and compute centroids of each activity based on chain coding mechanism and centroids point extraction. In body skin joints features, we estimate human body skin color to identify human body parts (i.e., head, hands, and feet) likely to extract joint points information. These joints points are further processed as feature extraction process including distance position features and centroid distance features. Lastly, self-organized maps are used to recognize different activities. Experimental results demonstrate that the proposed method is reliable and efficient in recognizing human poses at different realistic scenes. The proposed system should be applicable to different consumer application systems such as healthcare system, video surveillance system and indoor monitoring systems which track and recognize different activities of multiple users.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.9
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• pp.2205-2212
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• 2015

The aim of this study was to explore the acceptance factors of IPTV healthcare users as convergence service through experts's opinion. First, this study extracted expected indicators for the acceptance factors from brainstorming and literature review. Based on the expected indicators, the Delphi method was performed in order to explore the suitable acceptance factors. The reliability of collected data was evaluated through the criteria of CV(Coefficient of Variation) and CVR(Contents Validity Ratio) on the selected indicators. The results showed a significant acceptance factors timeliness, the following appeared as entertainment, fun and self-efficacy and more. In the future, such additional step as factor analysis targeting the user to verify the validity of the selected factors is required.

• Journal of Industrial Convergence
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• v.18 no.3
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• pp.109-116
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• 2020

The purpose of this study is to make suggestions for the infectious disease surveillance systems as part of the government's healthcare policy intended to minimize damage by implementing an appropriate and swift crisis management in the event of future infectious disease outbreaks. To that end, this descriptive study analyzes the infectious disease outbreaks and the management and control thereof in Korea and other countries, so as to suggest some approaches to infectious disease surveillance systems applicable to affected regions. The analysis results shed light on the causes of the spread of future infectious diseases over the past years, and the management systems that could possibly deal with the trial and error in the response policy. It seems crucial to roll out appropriate information, training and promotion programs as part of the national disaster response systems to prevent infectious diseases in relation to the roles of multiple relevant government agencies in the event of disasters especially amid the COVID-19 pandemic.