• Journal of the Korea Society of Computer and Information
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• v.16 no.3
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• pp.9-15
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• 2011

U-healthcare system has an aim to provide reliable and fast medical services for patient regardless of time and space by transmitting to doctors a large quantity of vital signs collected from sensor networks. Existing u-healthcare systems can merely monitoring patients' health status. In this paper, we describe the implementation and validation of a prototype of a u-health monitoring system based on a wireless sensor network. This system is easy to derive physiologically meaningful results by analyzing rapidly vital signs. The monitoring system sends only the abnormal data of examinee to the service provider. This technique can reduces the wireless data packet overload between a monitoring part and service provider. The real-time bio-signal monitoring system makes possible to implement u-health services and improving efficiency of medical services.

• IEMEK Journal of Embedded Systems and Applications
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• v.10 no.4
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• pp.221-231
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• 2015

This paper provides the trend of Internet of Things (IoT) for smart healthcare services and applications. IoT has provided a promising opportunity to build intelligent healthcare system and smart wearable applications by using the growing capability of wireless mobile devices, interactive sensors/actuators, and RFID technologies. For analysis of state-of-art technology of smart healthcare system, this paper includes comparative analysis and investigation of existing standard, network protocol, and devices, etc. In this paper, we examine the market trend of IoT healthcare. In particular, we examine the variety of IoT based healthcare type such as mobile, wearable device. After that, we examine the technologies of IoT healthcare such as standard, sensor, network and security. This survey contributes to better understanding of the challenges in existing IoT healthcare and further new light on future research directions.

• Journal of Satellite, Information and Communications
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• v.11 no.3
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• pp.18-23
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• 2016

Internet of things (IoT) is revolutionizing in the patient-Centered medical monitoring and management by authorizing the Smartphone application and data analysis with medical centers. The network connectivity is basic requirement to collect the observed human beings' health information from Smartphone to monitor the health from IoT medical devices in personal healthcare. The IoT environment built in Smartphone is very effective and does not demand infrastructure. This paper presents the smart phone deployed personal IoT architecture for Non-Invasive ECG Capturing. The adaptable IoT medical device cum Gateway is used for personal healthcare with big data storage on cloud configuration. In this approach, the Smartphone camera based imaging technique used to extract the personal ECG waveform and forward it to the cloud based big data storage connectivity using IoT architecture. Elaborated algorithm allows for efficient ECG registration directly from face image captured from Smartphone or Tablet camera. The profound technique may have an exceptional value in monitoring personal healthcare after adequate enhancements are introduced.

• IMMUNE NETWORK
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• v.19 no.1
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• pp.1.1-1.13
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• 2019

Systemic lupus erythematosus (SLE) is the prototypic systemic autoimmune disease characterized by production of autoantibodies to various nuclear antigens and overexpression of genes regulated by IFN-I called IFN signature. Genetic studies on SLE patients and mutational analyses of mouse models demonstrate crucial roles of nucleic acid (NA) sensors in development of SLE. Although NA sensors are involved in induction of antimicrobial immune responses by recognizing microbial NAs, recognition of self NAs by NA sensors induces production of autoantibodies to NAs in B cells and production of IFN-I in plasmacytoid dendritic cells. Among various NA sensors, the endosomal RNA sensor TLR7 plays an essential role in development of SLE at least in mouse models. CD72 is an inhibitory B cell co-receptor containing an immunoreceptor tyrosine-based inhibition motif (ITIM) in the cytoplasmic region and a C-type lectin like-domain (CTLD) in the extracellular region. CD72 is known to regulate development of SLE because CD72 polymorphisms associate with SLE in both human and mice and CD72^-/- mice develop relatively severe lupus-like disease. CD72 specifically recognizes the RNA-containing endogenous TLR7 ligand Sm/RNP by its extracellular CTLD, and inhibits B cell responses to Sm/RNP by ITIM-mediated signal inhibition. These findings indicate that CD72 inhibits development of SLE by suppressing TLR7-dependent B cell response to self NAs. CD72 is thus involved in discrimination of self-NAs from microbial NAs by specifically suppressing autoimmune responses to self-NAs.

• Journal of Korea Multimedia Society
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• v.18 no.6
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• pp.772-779
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• 2015

Advancing information technology have rapidly changed our service environment of life, culture, and industry. Computer information communication system is applied in medical, health, distribution, and business transaction. Smart is using new information by combining ability of computer and information. Although agriculture is labor intensive industry that requires a lot of hands, agriculture is becoming knowledge-based industry today. In agriculture field, computer communication system is applied on facilities farming and machinery Agricultural. In this paper, we designed and implemented application that provides personalized agriculture related information at the actual farming field. Also, this provides farmer a system that they can directly auction or sell their produced crops. We designed and implemented a system that parsing information of each seasonal, weather condition, market price, region based, crop, and disease and insects through individual setup on ubiquitous environment using location-based sensor network and processing data.

• Journal of Digital Convergence
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• v.11 no.3
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• pp.311-316
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• 2013

In order to maximize an efficient management of human cell resource under the cryogenic environments, in this paper, a middleware is introduced to support the function for multiple-perceiving RFID tags of intelligent sample case which can share medical information between sensor network devices. Optimized user interface is also designed for that. On based of the designing, special tasks required of a genetic resource working process can be processed on Complex Machine.

• Journal of Magnetics
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• v.22 no.1
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• pp.150-154
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• 2017

A novel high precision spatial positioning method utilizing the electromagnetic momentum, i.e., Electromagnetic Momentum Positioning (EMP), is proposed in this paper. By measuring the momentum of the electromagnetic field around the small current loop, the relative position between the sensor and the current loop is calculated. This method is particularly suitable for the application of close-range and high-precision positioning, e.g., data gloves and medical devices in personal healthcare, etc. The simulation results show that EMP method can give a high accuracy with the positioning error less than 1 mm, which is better than the traditional magnetic positioning devices with the error greater than 1 cm. This method lays the foundation for the application of data gloves to meet the accurate positioning requirement, such as the high precision interaction in Virtual Reality (VR), Augmented Reality (AR) and personal wearable devices network.

• Journal of Korea Society of Digital Industry and Information Management
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• v.4 no.3
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• pp.1-8
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• 2008

Recently, a great many people are concerned about promoting their health because medical science and scientific technology has become much larger and more develop. Thus, the person who interested in health wants to confirm his condition whatever he may take a meal or exercise. But it is disappointed of our expectation. By reason that many people doesn't know what changes will occur in their body. In this paper, we are going to introduce our Health Care Managing System which could display a physical variation, in addition, we will also propose how to control serial data from wireless sensors. We implemented this system using ZigbeX and Java application.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.667-669
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• 2021

Recently, RFID/USN technology has been applied in various fields such as logistics, environment, education, home network, disaster prevention, military, and medical care, but despite the remarkable development of RFID/USN technology, it is difficult to apply it to marine industry due to the characteristics of poor marine environment. Therefore, satellites are mainly used in the marine sector, and existing communication networks are used in the coast, so measures for forming a shelf-only short-range network in the ocean are being considered. In this paper, we consider the use of drones as mobile base stations of USN as a base station role using USN in existing PS-LTE and LTE networks.Since autonomous navigation vessels are aiming for the intelligent system, the number of crew and labor force should be reduced and the function of autonomous network formation in the form of more stable and intelligent ICT convergence technology should be strengthened.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.4
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• pp.826-842
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• 2024

As 5G and AI continue to develop, there has been a significant surge in the healthcare industry. The COVID-19 pandemic has posed immense challenges to the global health system. This study proposes an FL-supported edge computing model based on federated learning (FL) for predicting clinical outcomes of COVID-19 patients during hospitalization. The model aims to address the challenges posed by the pandemic, such as the need for sophisticated predictive models, privacy concerns, and the non-IID nature of COVID-19 data. The model utilizes the FATE framework, known for its privacy-preserving technologies, to enhance predictive precision while ensuring data privacy and effectively managing data heterogeneity. The model's ability to generalize across diverse datasets and its adaptability in real-world clinical settings are highlighted by the use of SHAP values, which streamline the training process by identifying influential features, thus reducing computational overhead without compromising predictive precision. The study demonstrates that the proposed model achieves comparable precision to specific machine learning models when dataset sizes are identical and surpasses traditional models when larger training data volumes are employed. The model's performance is further improved when trained on datasets from diverse nodes, leading to superior generalization and overall performance, especially in scenarios with insufficient node features. The integration of FL with edge computing contributes significantly to the reliable prediction of COVID-19 patient outcomes with greater privacy. The research contributes to healthcare technology by providing a practical solution for early intervention and personalized treatment plans, leading to improved patient outcomes and efficient resource allocation during public health crises.