• 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.

• Journal of Digital Convergence
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• v.17 no.2
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• pp.163-170
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• 2019

Over the past decade, the development of the Web explosively increased the data. Feature selection step is an important step in extracting valuable data from a large amount of data. This study proposes a novel opinion mining model based on combining feature selection (FS) methods with Word embedding to vector (Word2vec) and BOW (Bag-of-words). FS methods adopted for this study are CFS (Correlation based FS) and IG (Information Gain). To select an optimal FS method, a number of classifiers ranging from LR (logistic regression), NN (neural network), NBN (naive Bayesian network) to RF (random forest), RS (random subspace), ST (stacking). Empirical results with electronics and kitchen datasets showed that LR and ST classifiers combined with IG applied to BOW features yield best performance in opinion mining. Results with laptop and restaurant datasets revealed that the RF classifier using IG applied to Word2vec features represents best performance in opinion mining.

• Journal of the Korean Society of Radiology
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• v.13 no.4
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• pp.629-635
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• 2019

In term of the factors affecting psychosocial adjustment of breast cancer patients, their quality of life after surgical operation, radiation, and chemotherapy were systematically meta-analyzed. As a result, their qualities of life of the patients that had radiation therapy was the lowest right after the therapy, and gradually increased after the end of the therapy. However, after six months, their quality of life failed to reach the same level before the therapy. They had depression and side effects the most right after the therapy, and somewhat reduced them after the end of the therapy. In case of surgical operation, the more they were educated, the more they had psychosocial adjustment, and the more they had a medical examination and took out an insurance policy, the more they had psychosocial adjustment. In case of chemotherapy, their cognitive function is influenced so that they have impairments in memory, learning, and thinking stages. Since subjective cognitive impairment has a relationship with depression, it is necessary to monitor depression of chemotherapy patients. Given the results of this systematic meta-analysis, when three types of therapies (surgical operation, radiation therapy, and chemotherapy) are applied to patients with breast cancer, it is necessary to recognize their psychosocial adjustment, depression, anxiety, and quality of life in the nursing and radiation therapy fields and thereby to introduce an intervention program for a holistic approach.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.5
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• pp.241-251
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• 2019

In the present study, we investigated the effect of microcurrent against cognitive impairment in Alzheimer's disease (AD) mice model. The cognitive impairment was induced by intracerebroventricularly injection of amyloid beta ($A{\beta}$) to ICR mouse brain, and four kinds of micorocurrent wave were applied to AD mice. We observed the improved cognitive ability in microcurrent-applied AD mice through novel object recognition test and Morris water maze test, compared to $A{\beta}$-injected control group. The contents of malondialdehyde generated by $A{\beta}$ in the brain were also reduced by microcurrent application. These effects of microcurrent were related to the modulation of $A{\beta}$ producing and brain-derived neurotrophic factor (BDNF). Microcurrent down-regulated ${\beta}$-secretase, presenilin 1, and presenilin 2 which were related amyloidogenic pathway, and up-regulated human brain-derived neurotrophic factor in the mice brain, especially Wave4 group [STEP FORM wave form (0, 1.5, 3, 5V), wave superposition]. These results suggest that microcurrent application could provide help for improvement learning and memory ability, at least partly.

• Journal of Digital Convergence
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• v.17 no.4
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• pp.1-11
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• 2019

The purpose of this study is to investigate the effects of stress, social support, and resilience on the university life as predictors of college life adjustment. For this purpose, the questionnaires were administered to 145 college students in Busan, and correlation analysis, multiple regression analysis, and mediation analysis were conducted using SPSS 18.0. The main results are as follows. First, the significant factors influencing college life adjustment were stress (B = -.351, p <.01), social support(B = -.210, p <.05) resilience (B = .355, p < .01), 30.6% explanatory power, and resilience was the most influential factor. Among the sub-factors of stress, interpersonal stress and academic stress has a negative effect. In addition, friendship support had a statistically significant effect on social support, and resilience subscale was life satisfaction and cause analysis ability. In addition, stress was found to be partly mediated in the relationship between social support and college life adjustment. Based on this study, the strategies for lowering the stress, improving the resilience of the university students in order to improve the college life adaptation were provided.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.9 no.8
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• pp.783-790
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• 2019

Cognitive science attempts to explain human intelligence on the basis of success of artificial neural network, which is called connectionism. The neural network, e.g., deep learning, seemingly promises connectionism to go beyond what it is. But those(Fodor & Pylyshyn, Fodor, & McLaughlin) who advocate classical computationalism, or symbolism claim that connectionism must fail since it cannot represent the relation between human thoughts and human language. The neural network lacks systematicity, so any output of neural network is at best association or accidental combination of data plugged in input units. In this paper, I first introduce structure of artificial neural network and what connectionism amounts to. Second, I shed light on the problem of systematicity the classical computationalists pose for the connectionists. Third, I briefly introduce how those who advocate connectionism respond to the criticism while noticing Smolensky's theory of vector product. Finally, I examine the debate of computationalism and connectionism on systematicity, and show how the problem of systematicity contributes to the development of connectionism and computationalism both.

• Journal of radiological science and technology
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• v.44 no.5
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• pp.503-512
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• 2021

The purpose of this study is to check the extent to which "instruction of physician or dentist" defined in the Medical Service Technologists, etc. Act is applied in relation to radiography examination procedures for radiological technologists. In addition, it is intended to present basic data on the requirement to revise the Medical Service Technologists, etc. Act in the radiological technologist's duty area and scope of work, The subjects of this study were radiological technologists with license, and the response data were collected after sending the questionnaire link written on the online questionnaire form. The final number of respondents were 1,018, and the response rate was 6.8%. Most of the negative responses were "I have never received 'instruction' for radiologic examination by a physician or dentist, including a radiologist in a medical environment." There were a high perception that "the professionalism in radiation examination on radiological technologists are higher than that of a physician or dentist." They answered that the current continuing education has a great impact on maintaining and continuing professionalism and learning new knowledge in the radiology field. In addition, the radiological technologists provide a very high level of education in areas related to radiography procedure ethics such as patient care, patient safety, and patient privacy protection, as well as specialized fields such as radiation-related examination methods, radiography examination dose, and patient exposure dose. Radiological technologists replied that they were receiving it consistently. In conclusion, in the current medical environment, the 'instruction' of a physician or dentist cannot be seen as being realistically performed. The phrase 'instruction' of a physician or dentist as defined in the Medical Service Technologists, etc. Act is considered inappropriate in respect of the fact that the state recognizes the qualifications of the medical service technologist through a license. It is thought that revision to a new term suitable for the current medical environment is necessary.

• Journal of Convergence for Information Technology
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• v.10 no.12
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• pp.201-207
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• 2020

In this study, the practicality of unmanned aerial vehicle photography information was identified. Therefore, a total of four consecutive surveys were conducted on the field-level survey areas among the areas subject to photography using unmanned aerial vehicles, and the changes in crop conditions were analyzed using pictures of unmanned aerial vehicles taken during each survey. It is appropriate to collect and utilize photographic information by directly taking pictures of the survey area according to the time of the on-site survey using unmanned aerial vehicles in the field layer, which is an area where many changes in topography, crop vegetation, and crop types are expected. And it turned out that it was appropriate to utilize satellite images in consideration of economic and efficient aspects in relatively unchanged rice paddies and facilities. If the survey area is well equipped with systems for crop cultivation, deep learning can be utilized in real time by utilizing libraries after obtaining photographic data for a certain area using unmanned aircraft in the future. Through this process, it is believed that it can be used to analyze the overall crop and shipment volume by identifying the crop status and surveying the quantity per unit area.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.1
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• pp.379-384
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• 2021

Despite the numerous graduates being produced in practical music and history, which have not been shorter than 30 years, the employment of students remains an unsolvable task. Most graduates are employed by private institutes as music instructors or got involved in band activities, which are non-regular work and not stable. The rapid development of K-pop along with national strength has changed our lives as well. As a result, the cultural and artistic fields, which were previously recognized only as luxury, have rapidly become popular. In the form of learning, internalized and experiencing, not just watching has a huge impact on improving the quality of our lives. This is reflected in national policies including the operation of cultural arts, education programs in elementary, middle, and high schools, and art programs designed to improve the welfare of residents at local community centers. It is time to expand its job-horizon to the relevant fields, and thus focus on promoting programs related to obtaining certificates to help our students find jobs. In addition, the government should create a course for music teacher certification in the field of practical music that differs from the current music teacher certification of secondary schools. It is very urgent to establish the teaching course in four-year universities or graduate schools of practical music education.

• Journal of Internet of Things and Convergence
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• v.8 no.6
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• pp.115-122
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• 2022

Recently, the importance of CPR training for the layperson has been emphasized to improve the survival rate of out-of-hospital cardiac arrest patients. An accurate and realistic training strategy is required for the CPR training effect for laypersons. In this study, we develop an extended reality (XR) based CPR training system and evaluate its usability. The XR based CPR training system consisted of three applications. First, a 3D heart anatomy image registered to the manikin is transmitted to the smart glasses to guide the chest compression point. The second application provides visual and auditory information about the CPR process through smart glasses. At the same time, the smartwatch sends a vibration notification to guide the compression rate. The 'Add-on-kit' is a device that detects the depth and speed of chest compression via sensors installed on the manikin and sends immediate feedback to the smartphone. One hundred laypersons who participated in this study agreed that the XR based CPR training system has realism and effectiveness. XR based registration technology will contribute to improving the efficiency of CPR training by enhancing realism, immersion, and self-directed learning.