• The Journal of Information Systems
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• v.28 no.3
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• pp.159-202
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• 2019

Purpose The purpose of this study is to explain users' wearable healthcare device adoption using performance expectancy, effort expectancy, the hedonic motivation and price value of UTAUT2, and to identify the causal relationship between intention to use wearable healthcare device and innovation resistance formed by perceived risks. Design/methodology/approach The research model proposed in this study is based on UTAUT2(Extended Unified Theory of Acceptance and Use of Technology) and MIR(Model of Innovation Resistance). In specific, performance expectancy, effort expectancy, hedonic motivation and price value of UTAUT2 and innovation resistance formed by perceived risks of MIR are adopted in our research model. To validate the research model, we carry out the analysis of the survey data using Smart PLS 3.0 to test the hypotheses. Findings According to the empirical analysis results, this study confirms that the performance expectancy, effort expectancy, hedonic motivation, and price value have significant effects on the intention to use wearable healthcare devices. It also finds that perceived risk affects innovation resistance and in turn, innovation resistance affects the intention to use wearable healthcare devices.

• Journal of the Korea Society of Computer and Information
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• v.25 no.8
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• pp.81-87
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• 2020

In this paper, proposes a health status monitoring system for socially marginalized elderly households living alone. This system is implemented by collecting various PHR biometric signals and residential environment information through IoT devices. In addition, the company aims to establish a basic infrastructure that can understand the situation of lonely deaths and implement prevention programs by strengthening the predictive ability through data analysis of the DB server based on PHR and information collected from IoT sensors. The sensor consists of an environmental information collection sensor and a noncontact and wearable sensor for biometric signal collection. A gateway is required to transmit the collected data to the server, and the prototype is presented in this paper. The paper has a discussion purpose of policy task for expanding medical welfare service. The results of this study are believed to help expand services to the socially marginalized and improve the medical environment of the people.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.329-331
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• 2021

Recently, as various cases of using deep learning in the health-care field are increasing, functions such as electrocardiogram examination and body composition analysis through wearable device can be provided to provide rational decision-making and a process tailored to the individual. In order to utilize deep learning, it it most important to secure refined data, and this data is being made through human intervention or unsupervised learning. In this paper, we propose a model that conducts unsupervised learning by clusters according to gender and age using human body data such as chest and waist circumferences, which are easy to measure, and classifies them with CNN. For data, the 7th human body data provided by Korean Agency for Technology and Standards was used. Through this, it it thought that it can be applied to various application cases such as personalized body shape management service and obesity analysis.

• Journal of Digital Convergence
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• v.17 no.12
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• pp.249-254
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• 2019

According to the recent expansion of the network environment, the spread of smart devices is continuously increasing. With the spread of smart devices such as smart phones, smart pads and wearables, changes are taking place in smart technologies and IT convergence technologies. The development of smart technology is a key element of the 4th industrial technology. The Fourth Industrial Revolution expanded the new service-based industry by adding intelligence to residential, industrial and production environments using IT convergence and smart devices. Research on providing various services using smart technologies, such as smart home, smart factory, smart farm, and smart healthcare, is being conducted in variety. In particular, There is a sharp rise in smart homes due to the proliferation of IoT devices and the growth of sensor technology, control technology, applications, data management, and cloud services. Smart home services using smart technology provide residents with convenient, beneficial services and environments. Smart home service has complemented the existing home network service, but there still are flaws to be modified. In other words, the spread of smart devices, the development of service provider-oriented services, and the interlocking of services have limitations in providing services in consideration of user environment and user state. In order to solve this problem, this study proposes a smart home service system that considers the situation of the elderly.

• Journal of Internet Computing and Services
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• v.22 no.1
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• pp.99-107
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• 2021

With the recent establishment of a ubiquitous-based medical and healthcare environment, the medical information system for obtaining situation information from various sensors is increasing. In the medical information system environment based on context-awareness, the patient situation can be determined as normal or emergency using situational information. In addition, medical staff can easily access patient information after simple user authentication using ID and Password through applications on smart devices. However, these services of authentication and patient information access are staff-oriented systems and do not fully consider the ubiquitous-based healthcare information system environment. In this paper, we present a authentication service model based context-awareness system for providing situational information-driven authentication services to users who access medical information, and implemented proposed system. The authentication service model based context-awareness system is a service that recognizes patient situations through sensors and the authentication and authorization of medical staff proceed differently according to patient situations. It was implemented using wearables, biometric data measurement modules, camera sensors, etc. to configure various situational information measurement environments. If the patient situation was emergency situation, the medical information server sent an emergency message to the smart device of the medical staff, and the medical staff that received the emergency message tried to authenticate using the application of the smart device to access the patient information. Once all authentication was completed, medical staff will be given access to high-level medical information and can even checked patient medical information that could not be seen under normal situation. The authentication service model based context-awareness system not only fully considered the ubiquitous medical information system environment, but also enhanced patient-centered systematic security and access transparency.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.6
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• pp.126-134
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• 2015

A sleep disorder is being recognized as one of the major health issues related to high levels of stress. At the same time, interests about quality of sleep are rapidly increasing. However, diagnosing sleep disorder is not a simple task because patients should undergo polysomnography test, which requires a long time and high cost. To solve this problem, an accelerometer embedded wrist-worn device is being considered as a simple and low cost solution. However, conventional methods determine a state of user to "sleep" or "wake" according to whether values of individual section's accelerometer data exceed a certain threshold or not. As a result, a high miss-classification rate is observed due to user's intermittent movements while sleeping and tiny movements while awake. In this paper, we propose a novel method that resolves the above problems by employing a dynamic classifier which evaluates a similarity between the neighboring data scores obtained from SVM classifier. A performance of the proposed method is evaluated using 50 data sets and its superiority is verified by achieving 88.9% accuracy, 88.9% sensitivity, and 88.5% specificity.