• Journal of Korea Multimedia Society
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• v.13 no.2
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• pp.225-236
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• 2010

This study proposes a method for the recognition of emergency situation according to the bimodal information of camera image sensor and gravity sensor. This method can recognize emergency condition by mutual cooperation and compensation between sensors even when one of the sensors malfunction, the user does not carry gravity sensor, or in the place like bathroom where it is hard to acquire camera images. This paper implemented HMM(Hidden Markov Model) based learning and recognition algorithm to recognize actions such as walking, sitting on floor, sitting at sofa, lying and fainting motions. Recognition rate was enhanced when image feature vectors and gravity feature vectors are combined in learning and recognition process. Also, this method maintains high recognition rate by detecting moving object through adaptive background model even in various illumination changes.

• Journal of the Korea Society of Computer and Information
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• v.14 no.2
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• pp.181-190
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• 2009

This paper presents a multimodal emergency recognition system based on visual information, audio information and gravity sensor information. It consists of video processing module, audio processing module, gravity sensor processing module and multimodal integration module. The video processing module and gravity sensor processing module respectively detects actions such as moving, stopping and fainting and transfer them to the multimodal integration module. The multimodal integration module detects emergency by fusing the transferred information and verifies it by asking a question and recognizing the answer via audio channel. The experiment results show that the recognition rate of video processing module only is 91.5% and that of gravity sensor processing module only is 94%, but when both information are combined the recognition result becomes 100%.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.2
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• pp.254-259
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• 2011

For the emergency detecting in general CCTV environment of our daily life, the monitoring by only images through CCTV information occurs some problems especially in cost as well as man power. Therefore, in this paper, for detecting emergency state dynamically through CCTV as well as resolving some problems, we propose a detection and recognition method for emergency and non-emergency speech by GMM. The proposed method determine whether input speech is emergency or non-emergency speech by global GMM. If emergeny speech, local GMM is performed to classify the type of emergency speech. The proposed method is tested and verified by emergency and non-emergency speeches in various environmental conditions.

• Journal of Digital Convergence
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• v.18 no.5
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• pp.325-336
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• 2020

This study identified the perception and coping ability of an emergency involving the elderly for facility directors in charge of services in long-term care facilities, and used it as basic data for developing educational programs and policy data for improving the ability of emergency facilities. The subjects were 192 directors of elderly care facilities and home care centers. Data were collected from March 15 to April 20, 2019 and analyzed using the SPSS WIN 25.0 program. Data analysis was performed using t-test, One-way ANOVA, Pearson's correlation coefficient, Scheffe, and multiple linear regression. The results revealed 97.4% of emergency experience, 6.16 points of emergency perception, and 62% of correct answers, and coping ability of an emergency was 69.61 ± 13.537. The negative correlation between emergency experience and ability to cope with emergencies(r=-.202, p= .005) was the long-term care facility type(β = 8.253, p<.001). Overall, an education program considering the type of long-term care facility is needed when applying emergency education for facility directors.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.3
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• pp.415-420
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• 2009

For the emergency detecting in general CCTV environment of our daily life, the monitoring by only images through CCTV information occurs some problems especially in cost as well as man power. Therefore, in this paper, for detecting emergency state dynamically through CCTV as well as resolving some problems, we propose our advanced speech recognition system. For the purpose of it, we adopt HMM(Hidden Markov Model) in our system to do a feature extraction. Also, we adopt Wiener filter technique for noise elimination in many information coming from on CCTV environment. In this paper, our system send only the emergency speech information to a manager to deal with emergency state effectively.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.3
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• pp.242-248
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• 2017

The purpose of this research is investigate the perception of first aid and differentiate between factors. The research subjects were 188 childcare teachers who work for day care centers in Chungchungnam-do, Republic of Korea and 70 pre-service childcare teachers who major in childcare in colleges in the same area. Collected data were converted to frequency and percentage, and cross correlation analysis was conducted for difference verification. The survey results are as follows. First, regarding the perception of first aid, there was a difference between childcare teachers and pre-service childcare teachers in their ability to cope with an emergency situation and responses to questions regarding frequent emergency situation. Second, the percentage of incorrect answers was high in the questions about impaction, poisoning, and biting. There were significant differences between the answers of childcare teachers and pre-service childcare teachers in questions about bleeding(injury), high fever, and CPR. In all of these questions, the percentage of correct answers of childcare teachers was higher. Regarding this result, clinical training for various emergency situations is needed.

• 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.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.11a
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• pp.807-809
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• 2020

기존의 CCTV 관리 체계는 사건·사고에 대한 신속한 조치가 불가능하고 정황 파악이나 증거자료 확보 등 사후조치의 성격이 강하다. 본 논문에서는 Mask R-CNN(Regions with CNN)을 이용하여 CCTV가 읽어 들이는 객체가 응급상황인지 판단하는 방법을 제시한다. 사람으로 인식되는 영역을 다층 퍼셉트론(MLP, Multi-Layer Perceptron)으로 학습시켜 해당 대상이 처한 상황을 인지하고 응급상황으로 인식되는 상황이 지속될 경우 관리 모니터를 통해 사용자에게 알림을 준다. 본 연구를 통해 실시간 상호작용적인 CCTV 관리 체계를 구축하여 도움이 필요한 사람의 골든타임을 놓치지 않게 될 것으로 기대한다.

• Proceedings of the Korea Contents Association Conference
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• 2012.05a
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• pp.289-290
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• 2012

본 연구는 다양한 무선센서를 이용하여 노인의 생활 패턴을 관찰하여 응급상황이 발생 했을 때 변화된 패턴을 인식하는 시스템에 대한 연구이다. 노인인구의 증가와 함께 독거노인의 인구도 증가하고 있다. 노인의 일상 상활을 센서를 이용하여 간접적으로 관찰하여 생활 패턴을 저장하였다가 일상과 다른 변화를 감지하여 응급상황을 판단하고 보호자나 응급센터에 신속히 연락을 할 수 있게 하고자 한다, 무선 센서를 사용함으로 설치가 간편하고 간접적인 관찰을 하므로 당사자가 불편하지 않도록 하였다.

• Journal of Internet Computing and Services
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• v.22 no.1
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• pp.99-107
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• 2021

With the recent establishment of a ubiquitous-based medical and healthcare environment, the medical information system for obtaining situation information from various sensors is increasing. In the medical information system environment based on context-awareness, the patient situation can be determined as normal or emergency using situational information. In addition, medical staff can easily access patient information after simple user authentication using ID and Password through applications on smart devices. However, these services of authentication and patient information access are staff-oriented systems and do not fully consider the ubiquitous-based healthcare information system environment. In this paper, we present a authentication service model based context-awareness system for providing situational information-driven authentication services to users who access medical information, and implemented proposed system. The authentication service model based context-awareness system is a service that recognizes patient situations through sensors and the authentication and authorization of medical staff proceed differently according to patient situations. It was implemented using wearables, biometric data measurement modules, camera sensors, etc. to configure various situational information measurement environments. If the patient situation was emergency situation, the medical information server sent an emergency message to the smart device of the medical staff, and the medical staff that received the emergency message tried to authenticate using the application of the smart device to access the patient information. Once all authentication was completed, medical staff will be given access to high-level medical information and can even checked patient medical information that could not be seen under normal situation. The authentication service model based context-awareness system not only fully considered the ubiquitous medical information system environment, but also enhanced patient-centered systematic security and access transparency.