• Journal of the Korea Society of Computer and Information
• /
• v.23 no.10
• /
• pp.119-125
• /
• 2018

The purpose of this study was to investigate the effect of health care on the body composition and metabolic Syndrome risk factors in male office workers. The subjects of this study were 30~40's male office workers and their physical activities were increased by mobile healthcare. The date analysis in this study was carried out paired t­test using SPSS 20.0 version(${\alpha}=.05$). The result of study were as follow: First. body composition kg(p<.015), BMI(p<.041), WC(p<.026) were significantly decreased after Increase in Physical Activity Using Mobile Health Care, although these did not reach statistical significance, SMM(p<.123), BF(p<.059) was slightly increased and decreased trend. Second, SBP(p<.300), DBP(p<.384) was slightly decreased trend and BS(p<.034) were significantly decreased after Increase in Physical Activity Using Mobile Health Care, Third, plasma TC(p<.015), TG(p<.003), LDL-C)(p<.000) were significantly decreased after Increase in Physical Activity Using Mobile Health Care and plasma HDL-C (p<.003) were significantly increased. These results suggest that increased physical activity using mobile health care has a positive effect on the body composition and metabolic syndrome index in male office workers. Sedentary lifestyles could be changed by Continuous feedback using mobile healthcare.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.46 no.6
• /
• pp.36-42
• /
• 2009

In the mobile device environment, the context-aware computing has been emerging as a core technology of ubiquitous computing. Compared with a desktop computer, a user interface and resource of mobile device is very limited. Traditional desktop-based user interface has been developed on the basis that a user's activity is static state. In contrast, mobile devices are not able to utilize representative desktop-based interaction mechanisms such as a keyboard and mouse, not only because the activity of a user is dynamic state, but mobile devices have limited resources and small LCD display. In this paper, we introduce an intelligent control system for the mobile device that can utility effectively the limited resource and complement the poor user interface by using an accelerometer being able to sense the physical activity and posture. The proposed system can estimate the user activity, static and dynamic states, and posture watching the PDA at the same time, and the proposed intelligent control system as its application, the backlight ON/OFF on the PDA, is run by the result of the user's behavior.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.47 no.2
• /
• pp.61-68
• /
• 2010

As a result of the growth of mobile devices such as PDA and cellular phone, a user can utilize various digital contents everywhere and anytime. However, mobile devices have the limited resources and interaction mechanisms. This paper introduces the schema for a user activity estimation and its application in order to overcome the poor user interface and limited resource problems. We are able to supplement lacking the user interface of mobile devices by using the user activity estimation proposed in this paper, and its application is a intelligent control interface for the display power on or off which can effectively utility the battery of the mobile device.

• Journal of Information Technology Services
• /
• v.13 no.4
• /
• pp.397-410
• /
• 2014

For mobile service organizations, it is one of the most important tasks to reflect users' opinions rapidly and accurately. In this study, the process is defined to elicit requirements of software/system improvement for mobile application by extracting and refining from users' opinion in mobile app, and detailed activities procession method in this processing are also proposed. The process consists of 3 activities to get requirements of software/system improvement for mobile app. First activity is to transform mobile app to software structure and define term dictionary. Second activity is to elicit simple sentences based on software from users' opinion and refine them. The last activity is to integrate and adjust refined requirements. To verify the usability and validity of the proposed process and the methods, the outputs of manual processing and semi-automated processing were compared. As a result, efficiency and improvement possibility of the process were confirmed through extraction ratio of requirements, comparison of execution time, and analysis of agreement ratio.

• Journal of Korea Society of Digital Industry and Information Management
• /
• v.8 no.4
• /
• pp.35-41
• /
• 2012

Recently, universities have difficulties in operating the normal curriculum because fresher's basic academic ability is declined. It causes campus misfits so managing students is also not an easy matter. The education system that focuses only on college entrance exams is one of the reasons why this phenomenon occurred. Activity with self-directed Learning Community to know learning level themselves and execute systematic studying habit is essential for improving this problem. This activity can help students understanding and having interest in class and be motivated to study. But it had burdened tutors with submitting activity report in written form. In this paper, we suggest the Mobile Based Activity Report Submission System which can be the solution of the problem that the Self-directed Learning Community System has. This system reduces the emotional burden to write the reports and manages them efficiently.

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

• The Journal of the Korea Contents Association
• /
• v.20 no.3
• /
• pp.440-451
• /
• 2020

The purpose of this study is to present a method of Korean speaking activity utilizing mobile learning. It can compensate for the shortcomings of Korean speaking classes. Currently, speaking classes in the Korean as a foreign language site are difficult to interact individually and immediately because there are one professor and several learners. So it is hard for learners to improve their speaking ability in the actual class. However, it is helpful for learners to receive instant and individual feedback and check their pronunciation, intonation and speed. by using mobile learning. Therefore, This study consists of main-activity that can correct their pronunciation, post-activity that can make free dialogues through communication between learner and learner. So learners can improve their pronunciation accuracy and fluency as well as composition of conversation. This activity was applied to the actual class and after that, the satisfaction and opinions of learners were investigated. The results showed that many learners responded positively, but also suggested that they need to supplement mobile-learning activities in classrooms and future APP developments.

• Journal of the Korean Society of Systems Engineering
• /
• v.17 no.2
• /
• pp.98-105
• /
• 2021

As the average life expectancy is rising, the population is aging and the number of chronic diseases is increasing. This has increased the importance of healthy life and health management, and interest in mobile health services is on the rise thanks to the development of ICT(Information and communication technologies) and the smartphone use expansion. In order to meet these interests, many mobile services related to daily health are being launched in the market. Therefore, in this study, the characteristics of users who actually use mobile health services were analyzed and a predictive model applied with machine learning modeling was developed. As a result of the study, we developed a prediction model to which the decision tree and ensemble methods were applied. And it was found that the mobile health service users' continued use can be induced by providing features that require frequent visit, suggesting achievable activity missions, and guiding the sensor connection for user's activity measurement.

• Proceedings of the Korea Society of Design Studies Conference
• /
• 2005.05a
• /
• pp.48-49
• /
• 2005

• Proceedings of the Korea Society of Design Studies Conference
• /
• 2005.05a
• /
• pp.56-57
• /
• 2005