• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.873-874
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• 2006

In this paper, we have developed a ventricular fibrillation detection system based on DSP processor. The developed system was able to detect VF in real time correctly and quickly. We compared the performance of the floating point simulation with that of fixed point simulation. The computational cost of fixed point simulation was remarkably reduced than that of floating point simulation.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.11
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• pp.3-11
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• 2013

In smart healthcare service, the accurate and prompt emergency detection and notification are very critical to patients' lives. Since these detection and notification of emergency situation are usually performed by the medical staffs, it is difficult to simultaneously support many patients in real-time. This article presents a methodology for emergency bio-data transmission for smart healthcare using personalized emergency policy. It consists of three steps: In step 1, the bio-data is collected by wireless body area network. In step 2, the decision on emergency is made using personalized emergency policy. In step 3, the emergency message including the health condition information is converted between IEEE 11073 PHD message and HL7 CDA. By doing this, the emergency status of the individual bio-data collected from wireless body area network is detected automatically using personalized emergency policy. When the emergency is detected, the quick emergency rescue service can be provided to the patient by delivering to the emergency notification and the emergency bio-data. We have verified the service and functions of the proposed system architecture by realizing it.

• Journal of Internet Computing and Services
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• v.23 no.2
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• pp.37-44
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• 2022

With the outbreak of COVID-19, non-face-to-face activities such as remote learning and telecommuting have increased rapidly. As a result, the number of people staying at home and the number of hours spent inside the house have also increased since the pandemic. Our team had previously worked on methods for detecting abnormal conditions in a person's health in various circumstances within the house by converging single sensor-based algorithms. In our previous research, we installed IoT sensors indoors to detect people emergency situations requiring aids, the scope of detection was limited to indoor space due to the limitation in sensors. In this study, we have come up with a system that integrates our previous study with a new method for detecting abnormal conditions in outdoor environments using outdoor security cameras and wearable devices. The proposed system enables users to be notified of emergency situations in both indoor and outdoor areas and respond to them as quickly as possible.

• The Journal of Korea Robotics Society
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• v.3 no.3
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• pp.186-193
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• 2008

We propose a novel real-time obstacle avoidance method for rescue robots. This method, named the ELA(Emergency Level Around), permits the detection of unknown obstacles and avoids collisions while simultaneously steering the mobile robot toward safe position. In the ELA, we consider two sensor modules, PSD(Position Sensitive Detector) infrared sensors taking charge of obstacle detection in short distance and LMS(Laser Measurement System) in long distance respectively. Hence if a robot recognizes an obstacle ahead by PSD infrared sensors first, and judges impossibility to overcome the obstacle based on driving mode decision process, the order of priority is transferred to LMS which collects data of radial distance centered on the robot to avoid the confronted obstacle. After gathering radial information, the ELA algorithm estimates emergency level around a robot and generates a polar histogram based on the emergency level to judge where the optimal free space is. Finally, steering angle is determined to guarantee rotation to randomly direction as well as robot width for safe avoidance. Simulation results from wandering in closed local area which includes various obstacles and different conditions demonstrate the power of the ELA.

• The Journal of the Korea Contents Association
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• v.6 no.9
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• pp.50-59
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• 2006

This paper proposes an automatic video monitoring system and its application to emergency detection by analyzing human behavior using neural network. The object area is identified by subtracting the statistically constructed background image from the input image. The identified object area then is transformed to the feature vector. Neural network has been adapted for analyzing the human behavior using the feature vector, and is designed to classify the behavior in rather simple numerical calculation. The system proposed in this paper is able to classify the three human behavior: stand, faint, and squat. Experiment results shows that the proposed algorithm is very efficient and useful in detecting the emergency situation.

• Proceedings of the KSR Conference
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• 2009.05a
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• pp.457-465
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• 2009

Subway system including light-rail transit system is the representative public urban transportation. Accordingly, Nothing is more important than the safety operation as well as passengers' convenience. Due to the fire disaster of Daegu subway on 2003, more strict regulations of detecting fire and of conducting on emergency circumstances. However, regardless of this improved management, it was reported that installations of lots of fire-detection facilities may be harm than good to operate subway system due to frequent malfunction of some fire-detection facilities. It may cause mis-alarm for fire and induces abnormal operation of subway due to the strict regulation; the train shall be stopped on emergency circumstances. Therefore, in this paper, new scheme on surveiling breaking-out-of fire in the station is suggested with new IT technologies, Wireless Sensor Network(WSN) and CCTVs. which were integrated with an intelligent software. This intelligent system enables to surveil breaking-out-of fire in real time through sensor network technology and watch the emergency site on CCTV as well. Through this system, subway organizers could cope with the emergency circumstance rapidly as well as judge precisely whether fire breaks out or not.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.9 no.5
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• pp.101-107
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• 2009

In this paper, we describe a method of detecting emergency situation using images from surveillance cameras and propose a mobile robot tracking system for detailed examination of that situation. We are able to track a few persons and recognize their actions by an analyzing image sequences acquired from a fixed camera on all sides of buildings. When emergency situation is detected, a mobile robot moves and closely examines the place where the emergency is occurred. In order to recognize actions of a few persons using a sequence of images from surveillance cameras images, we need to track and manage a list of the regions which are regarded as human appearances. Interest regions are segmented from the background using MOG(Mixture of Gaussian) model and continuously tracked using appearance model in a single image. Then we construct a MHI(Motion History Image) for a tracked person using silhouette information of region blobs and model actions. Emergency situation is finally detected by applying these information to neural network. And we also implement mobile robot tracking technology using the distance between the person and a mobile robot.

• The Korean Journal of Emergency Medical Services
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• v.1 no.1
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• pp.20-27
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• 1997

This study of the positive rates of HBsAg, anti-HBs among 119 Emergency Medical Technicians and Rescuers who were working in Pohang, Kyoung-Buk, Korea was performed from March to June 1997 according to three methods of EIA, RPHA and PHA. The number of samples were 201. The results were as follows; The overall positive rate of HBs Ag by EIA and RPHA methods were 4.98%, 5.47%, the overall positive rate of anti-HBs by EIA and PHA methods were 58.71%, 63.68%. In detecting of HBs Ag, concurrence rate, sensitivity, specificity and predictability of PHA to EIA were 99.5%, 90.9%, 100% and 99.75% respectively. In detecting of anti-HBs, concurrence rate, sensitivity, specificity and predictability of PHA to EIA were 91.04%, 89%, 94.5% and 89.87% respectively. Based on this study, there were no significant diffrences in the positive rates of HBsAg and Anti-HBs in 119 Emergency Medical Technicians and Rescurers in Pohang, Korea as compared with the general population according to other studies. In terms of concurrence rate, sensitivity, specificity and the costs of RPHA with EIA for detecting HBsAg, RPHA is more cost effective than EIA for mass screening of HBsAg detection. Also, In terms of concurrence rate, sensitivity and specificity of PHA with EIA, PHA is more cost effective and less problems of procedure than the EIA for mass screening of Anti-HBs detection.

• Journal of Advanced Navigation Technology
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• v.18 no.6
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• pp.633-639
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• 2014

Recently the study about a smart health care which is combined IT with BT to provide a variety of health care services are being actively investigated. In order to provide the best possible emergency medical services in a short period of time, it is necessary that the rapid emergency measures in the event of an emergency essential. In this paper, we propose an emergency medical service platform to take effective first aid to person who has a NFC tag or NFC-enabled mobile smart phones in an accident. Using NFC, it is possible to help without physical contact to the patient unconscious to emergency incidents such as falling down in everyday life. In this paper, we design and implement an mobile emergency medical system that can deliver first aid information ask for help in case of emergency.

• Proceedings of the Korean Society of Computer Information Conference
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• 2018.07a
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• pp.197-198
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• 2018

본 논문에서는 산업 IoT (IIoT) 환경에서 생산 설비 내 각 센서 노드의 데이터 이상 여부를 게이트웨이에서 판단하는 Emergency node 선정 모델을 제안하였다. 이 모델은 IIoT 환경이 적용된 생산 설비의 Emergency 상태 즉, 이상 동작으로 인한 온도, 진동 데이터 등의 비정상적인 수집을 구분하여 즉각적으로 대응할 수 있도록 하는 것을 목표로 한다. 본 논문에서는 분할 선형 회귀를 통하여 주기 내 데이터의 허용 범위를 계산하여 기존의 Threshold 방식보다 정확하고 범용적으로 Emergency node를 분류한다.