A Comparative Study on the Effect of Eccentric Viewing Training Using PC and VR Contents

Title : A Comparative Study on the Effect of Eccentric Viewing Training Using PC and VR Contents
Journal : International Journal on Advanced Science, Engineering and Information Technology
Authors : Dokyeong Lee, HwaMin Lee
Corresponding author : HwaMin Lee
DOI : https://doi.org/10.18517/ijaseit.11.2.12832

Abstract
The purpose of this study is to develop PC Eccentric Viewing Training (EVT) and VR EVT Content, to conduct experiments to verify the validity of contents first through non-disabled people, and to compare and analyze VR and PC contents. Both PC and VR contents were produced with UNITY. This content model assumes that reducing inaccurate saccades by eliminating eye movement helps improve reading accuracy. In addition, the two contents are implemented in the same way as VR and PC versions. The content consists of two steps, both PC and VR. The purpose of the content is to improve reading accuracy by improving the fixation stability of Preferred Retinal Locus (PRL) and reducing inaccurate Saccades. The experiment consisted of 12 persons (within maximum visual acuity less than 0.3), and they were assigned to the PC Content group and VR Content group. The experiment was conducted a total of 5 times, except for two weeks, which is the time to adapt PRL. The experimental results showed that the reading accuracy of the VR content group was higher. In addition, When comparing VR contents with PC contents, the group that conducted the training through PC contents showed a decrease in concentration as it progressed to 1-3 steps, and the score distribution also fell overall. In conclusion, the study compared VR and PC contents, and the effectiveness of contents was verified through experiments.

Acknowledgments
This research was financially supported by the “ICT Convergence Smart Rehabilitation Industrial Education Program” through the Ministry of Trade, Industry & Energy (MOTIE). The authors are grateful to Korea Institute for Advancement of Technology (KIAT) and the MSIP (Ministry of Science, ICT and Future Planning), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2020-2015-0-00403) supervised by the IITP (Institute for Information & communications Technology Promotion).

Deep Learning based Cardiac Arrest Prediction using Vital sign and Lab code

Title : Deep Learning based Cardiac Arrest Prediction using Vital sign and Lab code
Published in : The 12th International Conference on Internet (ICONI 2020)
Author : Minsu Chae, Sangwook Han, Geonil Yun, Hyo-Wook Gil, Min Hong, DoKyeong Lee, HwaMin Lee
Corresponding author : HwaMin Lee
Location : Jeju ShinhwaWorld, Jeju, Korea

Abstract
There are patients in hospitals who are hospitalized for a variety of reasons. We conducted a study on predicting cardiac arrest on patients at Soonchunhyang University Cheonan Hospital. We studied patients from 2016 to 2019. We used deep learning via the LSTM model and the GRU model. We check density of each input feature according to cardiac arrest. We compared only patients with vital signs and lab data. We removed DBP, SBP, BodyTemperature, AST, ALT, WBC, Creatinine, and Bilirubin variable density because there was little difference between those with cardiac arrest and other patients. We experimented with the LSTM model and GRU Model. In this paper, deep learning-based cardiac arrest prediction using vital signs and lab data has high precision. In particular, when using the GRU model, even if the cardiac arrest of 0 to 24 hours for each record is changed, it has a sensitivity of 60%.

Acknowledgments
This research was supported by the Bio & Medical Technology Development Program of the National Research Foundation (NRF) funded by the Korean government (MSIT) (No. NRF-2019M3E5D1A02069073) and supported by the MSIT(Ministry of Science and ICT), Korea, under the ITRC(Information Technology Research Center) support program(IITP-2020-2015-0-00403) supervised by the IITP(Institute for Information & communications Technology Planning & Evaluation)

크롤링을 이용한 자동매칭 게임톡 웹 서비스

제목 : 크롤링을 이용한 자동매칭 게임톡 웹 서비스
저자 : 반영태, 한상욱, 이도경, 윤건일, 이화민
게재지 : 2019년 한국정보처리학회 추계학술발표대회 논문집
교신저자 : 이화민
장소 : 제주도, 제주대학교
DOI : https://doi.org/10.3745/PKIPS.y2019m10a.1169

초록
최근 많은 이용자들이 음성채팅을 이용하여 게임을 즐긴다. 하지만 많은 사람들이 게임 내에서 지원하는 음성 채팅을 사용하지 않고 별도의 음성 프로그램을 사용하고 있다. 현재 게임 내 음성채팅과 외부 음성채팅 모두 편의 기능이 많이 부족하며, 가장 큰 문제점으로는 사용자 본인이 직접 음성 채팅에 참여하는 유저를 구해야 한다는 것이다. 본 논문에서는 이러한 불편한 상황을 없애기 위하여 자동으로 음성 채팅이 가능한 사람을 모집하여 좀 더 편안한 게임 환경을 제공할 수 있는 음성 채팅 웹 서비스를 개발 하였다. 웹 크롤링 기술을 이용하여 외부 커뮤니티등의 구인 글을 크롤링 하여 설정한 조건과 구인 조건이 일치하면 사이트 사용자 뿐 만 아니라 미사용자 간의 매칭도 빠르게 지원 하도록 개발하였다.

Acknowledgments
이 성과는 2017년도 정부(과학기술정보통신부)의 재원으로 한국연구재단의 지원을 받아 수행된 연구임(NRF-2017R1A2B4010570)
본 연구는 과학기술정보통신부 및 정보통신기술진흥센터의 대학ICT연구센터육성 지원사업의 연구결과로 수행되었음(IITP-2019-2015-0-00403)

Design of the Eccentric Viewing Training Contents for the Rehabilitation of the Peripheral Visual Function

Title : Design of the Eccentric Viewing Training Contents for the Rehabilitation of the Peripheral Visual Function
Published in : The International Conference on Big data, IoT, and Cloud Computing (BIC 2019)
Author : DoKyeong Lee, HwaMin Lee
Corresponding author : HwaMin Lee
Location : MAISONGLAD JEJU Hotel, Jeju, Korea

Abstract
This study proposes Eccentric Viewing Training Content for Peripheral Visual Function Training using PC rehabilitation contents produced by Unity. The disability caused by the living difficulties of central brigs in the central visual acuity. People with low vision who have central scotoma need constant visual rehabilitation. However, the existing OBVT(Office-based vision therapy) has low access to training and high difficulty for long-term training. To overcome this inefficiency, this study proposes a training content that can be an HVT(Home vision therapy). The purpose of this study is to improve the accessibility and efficiency of eccentric viewing training and to enable rehabilitation training within fun elements of the game domain.

Acknowledgments
This research was supported by the MSIT(Ministry of Science and ICT), Korea, under the ITRC(Information Technology Research Center) support program(IITP-2019-2014-1-00720) supervised by the IITP(Institute for Information & communications Technology Planning & Evaluation) and the “ICT Convergence Smart Rehabilitation Industrial Education Program” through the Ministry of Trade, Industry & Energy (MOTIE) and Korea Institute for Advancement of Technology (KIAT).