• 한국컴퓨터정보학회논문지
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• 제23권10호
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• pp.119-125
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• 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.

• 대한전자공학회논문지SP
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• 제46권6호
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• pp.36-42
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• 2009

모바일 단말 환경에서 상황 인식 컴퓨팅 기술은 유비쿼터스 컴퓨팅의 핵심 기술 중 하나이다. 상황 인식 컴퓨팅은 사용자의 활동에 따라 능동적으로 반응하는 컴퓨팅 응용들을 실현 가능하게 한다. 현재 모바일 단말은 데스크 탑 컴퓨터에 비해 사용자 인터페이스와 자원은 매우 제한적이다. 데스크 탑 사용자는 정지된 상태에서 사용자 인터페이스를 설계하는 반면에 모바일 사용자는 단말을 사용하는 동안 움직인다는 것을 가정해야 하기 때문에 기존의 대표적인 입출력 장치인 키보드와 마우스 같은 편리한 사용자 인터페이스를 제공할 수 없다. 본 논문에서는 인간이나 물체의 물리적인 활동 상태와 자세를 감지할 수 있는 가속도센서를 사용하여 모바일 단말에 적용함으로서 모바일 단말의 부족한 사용자 인터페이스를 보완하고 제한된 자원을 효율적으로 이용할 수 있는 지능형 제어 시스템을 소개한다. 제안된 시스템은 모바일 단말기 사용자의 활동 상태 (정적인 상태와 동적인 상태)와 모바일 단말을 보는 자세를 동시에 추정하였고, 그것의 응용인 지능형 제어 시스템은 사용자의 행동에 따라 모바일 단말기의 백라이트가 ON 또는 OFF 되는 것이다.

• 대한전자공학회논문지SP
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• 제47권2호
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• pp.61-68
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• 2010

PDA와 휴대폰과 같은 모바일 단말의 급속한 발달로 인해 사용자들은 자신이 좋아하는 다양한 디지털 콘텐츠를 이동 중에도 즐길 수 있게 되었다. 그러나 이들 단말의 경우 데스크 탑에 비해 사용자 인터페이스와 자원이 매우 제한적이다. 본 논문에서는 모바일 단말의 사용자 인터페이스와 자원 문제를 동시에 해결하기 위한 방안으로 사용자 활동 상태 추정과 그 응용을 소개한다. 이는 사용자의 활동을 기반으로 제어가 이루어지는 기법으로 사용자 활동 상태 추정 기법을 이용하여 모바일 단말의 부족한 사용자 인터페이스를 보완하고, 이에 대한 응용은 모바일 단말의 배터리를 효율적으로 이용할 수 있는 능동형 디스플레이 전원 제어 인터페이스이다.

• 한국IT서비스학회지
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• 제13권4호
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• pp.397-410
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• 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.

• 디지털산업정보학회논문지
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• 제8권4호
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• pp.35-41
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• 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)
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• 제13권4호
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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.

• 한국콘텐츠학회논문지
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• 제20권3호
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• pp.440-451
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• 2020

본 연구는 모바일 러닝이 한국어 말하기 수업의 단점을 보완할 수 있다 보고, 이를 효과적으로 활용할 수 있는 말하기 활동 방안을 제시하는 데에 그 목적이 있다. 현재 한국어 교육 현장에서 이뤄지는 말하기 수업은 교사 한 명과 학습자 여러 명의 수업 방식을 취하고 있기 때문에 개별적이고 즉각적인 상호작용이 어렵다. 따라서 교실 현장에서 학습자들의 말하기 능력을 향상시키기가 현실적으로 힘든 상황이다. 하지만 모바일 러닝을 활용하면 학습자가 자신의 발화를 녹음하여 즉각적이고 개별적인 말하기 평가를 받을 수 있다. 이에 본 연구는 모바일 기기를 사용하여 자신의 발음을 확인, 교정하며 발음의 정확성과 말하기의 유창성을 향상시킬 수 있는 본 활동과 이를 바탕으로 학습자간 경험 공유하기를 통해 담화 구성 능력을 향상시킬 수 있는 사후 활동으로 구성하였다. 이러한 활동 방안은 실제 교육 현장에도 적용하여 학습자들의 만족도와 모바일 러닝에 관한 의견 등을 조사하였는데 그 결과 모바일을 활용한 말하기 활동에 긍정적인 반응을 보인 학습자들이 많았으나 향후 APP 개발에 있어 보완해야 할 점들도 시사하고 있었다.

• 시스템엔지니어링학술지
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• 제17권2호
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• pp.98-105
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• 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.

• 한국디자인학회:학술대회논문집
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• 한국디자인학회 2005년도 춘계 학술발표대회 논문집
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• pp.48-49
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• 2005

• 한국디자인학회:학술대회논문집
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• 한국디자인학회 2005년도 춘계 학술발표대회 논문집
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• pp.56-57
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• 2005