• Journal of Information Technology Services
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• v.12 no.4
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• pp.435-447
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• 2013

If we use a smartphone to analyze and detect falling, it is a huge advantage that the person with a sensor attached to one's body is free from awareness of difference and limitation of space, unlike attaching sensors on certain fixed areas. In this paper, we suggested effective posture analysis of smartphone users, and fall detecting system. Suggested algorithm enables to detect falling accurately by using the fact that instantaneous change of acceleration sensor is different according to user's posture. Since mobile applications working on smart phones are low in compatibility according to mobile platforms, it is a constraint that new development is needed which is suitable for sensor equipment's characteristics. In this paper, we suggested posture analysis algorithm using smartphones to solve the problems related to user's inconvenience and limitation of development according to sensor equipment's characteristics. Also, we developed fall detection system with the suggested algorithm, using hybrid mobile application which is not limited to platform.

• Journal of Internet Computing and Services
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• v.11 no.2
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• pp.31-40
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• 2010

In this paper, we propose a service component based on active model for supporting a variety of u-healthcare application services. It implemented that component as a classification of function for developing healthcare application services. Especially we focus on the adaptive information service in integrated environment using a distributed object technologies of the various healthcare home service based on distributed object group framework. And we shows the service component applying to Healthcare application services such as healthcare home monitoring, mobile monitoring and web based monitoring. Also, we show the performance evaluation results such as response time, system load and network load.

• Healthcare Informatics Research
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• v.24 no.4
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• pp.387-393
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• 2018

Objectives: This paper describes an experience of implementing seamless service trials online and offline by adopting Internet of Things (IoT) technology based on near-field communication (NFC) tags and Bluetooth low-energy (BLE) beacons. The services were provided for both patients and health professionals. Methods: The pilot services were implemented to enhance healthcare service quality, improve patient safety, and provide an effective business process to health professionals in a tertiary hospital in Seoul, Korea. The services to enhance healthcare service quality include healing tours, cancer information/education, psychological assessments, indoor navigation, and exercise volume checking. The services to improve patient safety are monitoring of high-risk inpatients and delivery of real-time health information in emergency situations. In addition, the services to provide an effective business process to health professionals include surveys and web services for patient management. Results: Considering the sustainability of the pilot services, we decided to pause navigation and patient monitoring services until the interference problem could be completely resolved because beacon signal interference significantly influences the quality of services. On the other hand, we had to continue to provide new wearable beacons to high-risk patients because of hygiene issues, so the cost increased over time and was much higher than expected. Conclusions: To make the smart connected hospital services sustainable, technical feasibility (e.g., beacon signal interference), economic feasibility (e.g., continuous provision of new necklace beacons), and organizational commitment and support (e.g., renewal of new alternative medical devices and infrastructure) are required.

• Journal of Internet Computing and Services
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• v.9 no.3
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• pp.9-24
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• 2008

In this paper, we describes the design of software architecture supporting for healthcare context information service platform based on multi agent in home environment. In this platform, the DOGF supports the execute object and healthcare sensors and device's logical services grouping. JADE framework can support mobility in heterogeneous environment. The multi agents on platform order to support a healthcare context information service it will be able to divide. An agent collects an environment information from distributed devices. Another an agent follows mobile-device specific and it does a different service. And an agent where it manages like this. The mobile-proxy&agent is an interface part between DOGF and JADE, support data interchange or mobility pattern. For DOGF and JADE to provide healthcare context information service, we describes the design of multi agent software platform and multi agent classification by services. Finally we showed the system environments which is physical environments and prototype based on platfonn for healthcare context information services.

• Journal of The Korea Institute of Healthcare Architecture
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• v.28 no.1
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• pp.7-21
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• 2022

Purpose: As the pandemic period continues, various attempts are being made to new medical spaces in the medical society. Many hospitals, including existing general hospitals, have been effected by infected patients and are showing limitations in patient care capacity. Mobile-hospitals may be the starting point for the development of new environment in the medical society and healthcare facilities which are not replacing the role of existing hospitals. Mobile-hospitals can possibly respond to situations that require medical services and provide emergency care for various demands in connection with existing healthcare facilities. Methods: Through a total of five investigations/analysis, medical functions that can be inserted into mobile-hospitals based on modular architecture are set. The first is the analysis of domestic legal guidelines, the second is the analysis of previous studies, the analysis of emergency medical facilities and other medical spaces of hospitals to be compared, the fourth is the analysis of medical spaces of actual mobile hospital projects. Results: Through five analyses, medical functions applicable to the modular building platform were finally established. Mobile hospitals can be used not only in disaster sites such as infectious diseases, but also in medical underprivileged areas or general hospitals. Therefore, it is necessary to establish medical functions that meet the specificity of mobile hospitals along with the functions of existing fixed medical facilities. Furthermore, various studies such as use in international aid, use in normal times, and connection with other platform-based medical facilities are considered necessary. Implications: Through 5 strategies of analysis, 41 medical functions which can be applied to UNIT are decided and these functions will be placed where medical services will be required.

• Journal of Digital Convergence
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• v.15 no.3
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• pp.187-193
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• 2017

Objects Internet-based healthcare services provide healthcare and healthcare services, including measurement of user's vital signs, diagnosis and prevention of diseases, through a variety of object internet devices. However, there is a problem that new security vulnerability can occur when inter-working with the security weakness of each element technology because the internet service based on the object Internet provides a service by integrating various element technologies. In this paper, we propose a user privacy protection model that can securely process user's healthcare information from a third party when delivering healthcare information of users using wearable equipment based on IoT in a mobile environment to a server. The proposed model provides attribute values for each healthcare sensor information so that the user can safely handle, store, and store the healthcare information, thereby managing the privacy of the user in a hierarchical manner. As a result of the performance evaluation, the throughput of IoT device is improved by 10.5% on average and the server overhead is 9.9% lower than that of the existing model.

• Journal of information and communication convergence engineering
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• v.9 no.5
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• pp.562-566
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• 2011

As telecommunication technologies in telemedicine services are developed, the expeditious development of wireless and mobile networks has stimulated wide applications of mobile electronic healthcare systems. However, security is an essential system requirement since many patients have privacy concerns when it comes to releasing their personal information over the open wireless channels. Due to the invisible feature of mobile signals, hackers have easier access to hospital networks than wired network systems. This may result in several security incidents unless security protocols are well prepared. In this paper, we analyzed authentication and authorization procedures for healthcare system architecture to apply secure M-health systems in the hospital environment. From the analyses, we estimate optimal requirements as a countermeasure to its vulnerabilities.

• Journal of Positioning, Navigation, and Timing
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• v.13 no.1
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• pp.15-23
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• 2024

One of the core keywords in the fourth industrial revolution is convergence, and the convergence of the production, distribution, and consumption processes of services is particularly important. The convergence of user services is underway in various industrial fields including mobile communications, healthcare, mobility, artificial intelligence, etc. In order to offer these converged services efficiently, it is necessary to provide accurate user-centric location information, which can be obtained by employing the global navigation satellite system (GNSS). In addition, as we have entered the post-COVID era, the demand for various fields such as a healthcare, customized tourism services, and aviation services based on accurate location information is exploding. In this paper, we present the results of a case study on the current research trends of GNSS used in telemedicine services and AI & IoT fields, and also analyze these results.

• The Journal of Industrial Distribution & Business
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• v.10 no.6
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• pp.19-27
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• 2019

Purpose - The purpose of this study was to examine quality improvement priorities by examining not only quality classifications but also PCSI (Kano, 1984) of mobile app service qualities of general hospitals in the Metropolitan Area and offer potential improvements. Research design, data, and methodology - The study examined five of service qualities, including app design, reaction, convenience, safety and supply of information by precedent studies. 20 test items were selected. A total of 60 positive and negative questions to estimate customer satisfaction and PCSI was investigated. The author collected 300 copies from interviewees who made use of the app services of 13 general hospitals within one year, and classified quality factors by using table of quality assessment and also estimated the customer satisfaction index (Timko, 1993). The study made quality improvement priority by the PCSI index. Results - Reaction of the mobile app service quality ranked the highest PCSI, and payment safety ranked the highest, and customer support and supply of the information ranked high as well. It was observed that design ranked comparatively lower in these categories. Conclusion - Safety, reaction and supply of information should be prioritized to reorganize and improve the mobile app services.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.2060-2077
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• 2019

Recently, mobile healthcare services have attracted significant attention because of the emerging development and supply of diverse wearable devices. Smartwatches and health bands are the most common type of mobile-based wearable devices and their market size is increasing considerably. However, simple value comparisons based on accumulated data have revealed certain problems, such as the standardized nature of health management and the lack of personalized health management service models. The convergence of information technology (IT) and biotechnology (BT) has shifted the medical paradigm from continuous health management and disease prevention to the development of a system that can be used to provide ground-based medical services regardless of the user's location. Moreover, the IT-BT convergence has necessitated the development of lifestyle improvement models and services that utilize big data analysis and machine learning to provide mobile healthcare-based personal health management and disease prevention information. Users' health data, which are specific as they change over time, are collected by different means according to the users' lifestyle and surrounding circumstances. In this paper, we propose a prediction model of user physical activity that uses data characteristics-based long short-term memory (DC-LSTM) recurrent neural networks (RNNs). To provide personalized services, the characteristics and surrounding circumstances of data collectable from mobile host devices were considered in the selection of variables for the model. The data characteristics considered were ease of collection, which represents whether or not variables are collectable, and frequency of occurrence, which represents whether or not changes made to input values constitute significant variables in terms of activity. The variables selected for providing personalized services were activity, weather, temperature, mean daily temperature, humidity, UV, fine dust, asthma and lung disease probability index, skin disease probability index, cadence, travel distance, mean heart rate, and sleep hours. The selected variables were classified according to the data characteristics. To predict activity, an LSTM RNN was built that uses the classified variables as input data and learns the dynamic characteristics of time series data. LSTM RNNs resolve the vanishing gradient problem that occurs in existing RNNs. They are classified into three different types according to data characteristics and constructed through connections among the LSTMs. The constructed neural network learns training data and predicts user activity. To evaluate the proposed model, the root mean square error (RMSE) was used in the performance evaluation of the user physical activity prediction method for which an autoregressive integrated moving average (ARIMA) model, a convolutional neural network (CNN), and an RNN were used. The results show that the proposed DC-LSTM RNN method yields an excellent mean RMSE value of 0.616. The proposed method is used for predicting significant activity considering the surrounding circumstances and user status utilizing the existing standardized activity prediction services. It can also be used to predict user physical activity and provide personalized healthcare based on the data collectable from mobile host devices.