• Journal of Internet Technology
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• v.20 no.5
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• pp.1535-1545
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• 2019

Recently, the diverse kinds of applications in multimedia computing have been developed for visual surveillance, healthcare, smart cities, and security. Video conferencing is one of core applications among multimedia applications. The Quality of Service of video conferencing is a major issue, because of limited network traffic. Video conferencing allow a large number of users to converse with each other. However, the huge amount of packets are generated in the process of transmitting and receiving the photographed images of users. Therefore, the number of packets in video conferencing needs to be reduced. Video conferencing can be conducted in virtual reality by sending only the control signals of virtual characters and showing virtual characters based on the received signals to represent the users, instead of the photographed images of the users, in real time. This paper proposes a method that determines representative photographed images by analyzing the collected photographed images of users, using KMedoids algorithm and a decision tree, and expresses the users based on the analyzed images. The decision tree used for video conferencing are generated automatically using the proposed method. Given that the behaviors in the decision tree is added or changed considering photographed images, it is possible to reproduce the decision tree by photographing the behavior of the user in real-time. In an experiment conducted, 63 consecutively photographed images were collected and a decision tree generated by using the silhouette images of the photographed images. Indices of the silhouette images were utilized to express a subject and one index was selected using a decision tree. The proposed method reduced the number of comparisons by a factor of 3.78 compared with the traditional method that uses correlation coefficient. Further, each user's image could be outputted by using only the control image table of the image and the index.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.755-758
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• 2016

The use of smart phones has had a great impact on the mobile internet business. It shows a lot of growth in the healthcare sector not only commerce, advertising, billing, games, video content, media, amd O2O business. The United States has eased the regulations for healthcare apps smart phone devices in 2015, and China has established a five-year road map to solve shortage of doctors and hospital beds by utilizing mobile devices such as wearable in the same year. The application of wearable devices in the medical field is gradually increasing in Korea too, but there is a security problem as leading challenge. Security incidents in non-ICT sectors such as financial, medical, etc. have increased by using ICT each year. Personal information leakage is also increasing in field likely occurring the potential secondary damages such as financial fraud, illegal promotions, insurance and pharmaceutical companies abuse. In this study, we analyze malwares as the mobile threats, the five risks of mobile smart phone, mobile use cases and the mobile threat countermeasures for healthcare.

• Journal of Korea Society of Digital Industry and Information Management
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• v.16 no.4
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• pp.123-132
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• 2020

With increasing social interest in health and health promotion, the government and many organizations are conducting various health campaigns for the public. The public health campaign is aimed at protecting the people from the dangers of disease and contributing to a healthy life and also help establishing a healthy attitude and changing behavior as well. In addition, many researches have been carried out in order to enhance the advertising effect of campaigns aimed at forming preventive attitudes and to verify it by applying many theories. However, as a result, there is a significant lack of research regarding analysis of differences in the effectiveness of the campaign by media. This study is to analyze the effect of health campaign by the type of media which published health campaign advertisements that can affect prevention attitude. To meet the purpose of this study, The 15 print media were to examine the impact of media characteristics and types on participation in campaigns for health campaigns. Through this, we will present the role of the media as an efficient channel to encourage the formation and participation of the health attitude of campaign advertisements, and present significant implications in the selection of optimal media and the execution of campaign budgets.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.6
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• pp.2767-2780
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• 2016

Video-based human-activity recognition has become increasingly popular due to the prominent corresponding applications in a variety of fields such as computer vision, image processing, smart-home healthcare, and human-computer interactions. The essential goals of a video-based activity-recognition system include the provision of behavior-based information to enable functionality that proactively assists a person with his/her tasks. The target of this work is the development of a novel approach for human-activity recognition, whereby human-body-joint features that are extracted from depth videos are used. From silhouette images taken at every depth, the direction and magnitude features are first obtained from each connected body-joint pair so that they can be augmented later with motion direction, as well as with the magnitude features of each joint in the next frame. A generalized discriminant analysis (GDA) is applied to make the spatiotemporal features more robust, followed by the feeding of the time-sequence features into a Hidden Markov Model (HMM) for the training of each activity. Lastly, all of the trained-activity HMMs are used for depth-video activity recognition.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.12
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• pp.3991-4007
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• 2022

In the public healthcare, a computational system that can automatically and efficiently detect and classify falls from a video sequence has significant potential. With the advancement of deep learning, which can extract temporal and spatial information, has become more widespread. However, traditional 3D CNNs that usually adopt shallow networks cannot obtain higher recognition accuracy than deeper networks. Additionally, some experiences of neural network show that the problem of gradient explosions occurs with increasing the network layers. As a result, an enhanced three-dimensional ResNet-based method for fall detection (3D-ERes-FD) is proposed to directly extract spatio-temporal features to address these issues. In our method, a 50-layer 3D residual network is used to deepen the network for improving fall recognition accuracy. Furthermore, enhanced residual units with four convolutional layers are developed to efficiently reduce the number of parameters and increase the depth of the network. According to the experimental results, the proposed method outperformed several state-of-the-art methods.

• Healthcare Informatics Research
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• v.24 no.4
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• pp.309-316
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• 2018

Objectives: Both the valence and arousal components of affect are important considerations when managing mental healthcare because they are associated with affective and physiological responses. Research on arousal and valence analysis, which uses images, texts, and physiological signals that employ deep learning, is actively underway; research investigating how to improve the recognition rate is needed. The goal of this research was to design a deep learning framework and model to classify arousal and valence, indicating positive and negative degrees of emotion as high or low. Methods: The proposed arousal and valence classification model to analyze the affective state was tested using data from 40 channels provided by a dataset for emotion analysis using electrocardiography (EEG), physiological, and video signals (the DEAP dataset). Experiments were based on 10 selected featured central and peripheral nervous system data points, using long short-term memory (LSTM) as a deep learning method. Results: The arousal and valence were classified and visualized on a two-dimensional coordinate plane. Profiles were designed depending on the number of hidden layers, nodes, and hyperparameters according to the error rate. The experimental results show an arousal and valence classification model accuracy of 74.65 and 78%, respectively. The proposed model performed better than previous other models. Conclusions: The proposed model appears to be effective in analyzing arousal and valence; specifically, it is expected that affective analysis using physiological signals based on LSTM will be possible without manual feature extraction. In a future study, the classification model will be adopted in mental healthcare management systems.

• Journal of Industrial Convergence
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• v.22 no.9
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• pp.121-131
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• 2024

The purpose of this study is to supply evidences through scoping review of educational intervention studies for donning and doffing PPE to prevent healthcare-associated infection provided to nurses and student nurses in Korea. Through the search engines RISS, KISS, and DBpia, 12 articles were chosen by searching through theses and journals which were published before May 1, 2024. According to the study result, relevant studies were 12, and the education programs provided to donning and doffing PPE to prevent healthcare-associated infection were classified into 1) simulation based education, 2) video and practice based education, and 3) non-contact education. Knowledge, performance confidence, self-efficacy, performance, attitude, and awareness were confirmed as effects of the intervention. Based on the results of this study, there is necessity to develop more diverse teaching and learning methods and evaluation methods for donning and doffing PPE that can prevent healthcare-associated infection in infectious disease and emerging infectious disease situations, and repeatedly conduct research on educational intervention for donning and doffing PPE.

• Journal of Gastric Cancer
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• v.24 no.3
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• pp.327-340
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• 2024

Purpose: Results of initial endoscopic biopsy of gastric lesions often differ from those of the final pathological diagnosis. We evaluated whether an artificial intelligence-based gastric lesion detection and diagnostic system, ENdoscopy as AI-powered Device Computer Aided Diagnosis for Gastroscopy (ENAD CAD-G), could reduce this discrepancy. Materials and Methods: We retrospectively collected 24,948 endoscopic images of early gastric cancers (EGCs), dysplasia, and benign lesions from 9,892 patients who underwent esophagogastroduodenoscopy between 2011 and 2021. The diagnostic performance of ENAD CAD-G was evaluated using the following real-world datasets: patients referred from community clinics with initial biopsy results of atypia (n=154), participants who underwent endoscopic resection for neoplasms (Internal video set, n=140), and participants who underwent endoscopy for screening or suspicion of gastric neoplasm referred from community clinics (External video set, n=296). Results: ENAD CAD-G classified the referred gastric lesions of atypia into EGC (accuracy, 82.47%; 95% confidence interval [CI], 76.46%-88.47%), dysplasia (88.31%; 83.24%-93.39%), and benign lesions (83.12%; 77.20%-89.03%). In the Internal video set, ENAD CAD-G identified dysplasia and EGC with diagnostic accuracies of 88.57% (95% CI, 83.30%-93.84%) and 91.43% (86.79%-96.07%), respectively, compared with an accuracy of 60.71% (52.62%-68.80%) for the initial biopsy results (P<0.001). In the External video set, ENAD CAD-G classified EGC, dysplasia, and benign lesions with diagnostic accuracies of 87.50% (83.73%-91.27%), 90.54% (87.21%-93.87%), and 88.85% (85.27%-92.44%), respectively. Conclusions: ENAD CAD-G is superior to initial biopsy for the detection and diagnosis of gastric lesions that require endoscopic resection. ENAD CAD-G can assist community endoscopists in identifying gastric lesions that require endoscopic resection.

• Health Communication
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• v.13 no.2
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• pp.217-221
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• 2018

Background: There are practical difficulties to show exact clinical symptoms such as seizure to medical students at Clinical Performance Examination (CPX). We developed a new CPX case of child's seizure on video using smartphone. Methods: A total of 356 $4^{th}-year$ students of five universities in Daegue-Gyeongbuk and Gyeongnam area took the clinical skill examination from June $13^{th}$ to $17^{th}$ in 2016. Among them, 72 students took the new CPX case in June $15^{th}$ and 71 students filled out the questionnaire on whether the new CPX with smartphone video is helpful, authentic, difficult, and necessary for other CPX. All the questions were measured on 5-Likert scale. Results: Mean score of the new CPX was 57.1, lower than the mean scores of the other 11 CPX cases, 62.8. For the question "Smartphone videos helped to solve the problem", 45 students (63.4%) answered 'Very much'. For the question "Is it realistic compared to other questions?" 30 students (42.3%) and 25 students (35.2%) answered 'Very much' and 'Much'. For the question "Is it difficult compared with other questions?" 18 students (25.4%) and 26 students (36.6%) answered 'Very much' and 'Much'. As for the question "I would like to have more tests using smartphone video", 26 students (36.6%) answered 'So and so'. Conclusion: A majority of students responded that video presentation was helpful and authentic to figure out the CPX, whereas they assessed smartphone video was more difficult compared with other CPXs. Further, students were negative toward using smartphone video for the other CPXs.

• Journal of Sensor Science and Technology
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• v.24 no.1
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• pp.69-77
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• 2015

The Microsoft Kinect is a motion sensing input device which is widely used for many motion recognition applications such as fitness, sports, and rehabilitation. Until now, most of remote rehabilitation systems with the Microsoft Kinect have allowed the user or patient to do rehabilitation or fitness by following the motion of a video screen. However in this paper we propose a smart remote rehabilitation system with the Microsoft Kinect motion sensor and a wearable ECG sensor which can allow patients to offer monitoring of the individual's performance and personalized feedback on rehabilitation exercises. The proposed noble smart remote rehabilitation is able to monitor and measure the state of the patient's condition during rehabilitation exercise, and transmits it to the prescriber. This system can give feedback to a prescriber, a doctor and a patient for improving and recovering motor performance. Thus, the efficient rehabilitation training service can be provided to patient in response to changes of patient's condition during exercise.