• 한국HCI학회:학술대회논문집
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• 2007.02a
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• pp.455-460
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• 2007

본 논문에서는 지능형 주택의 필수 요소 기술의 하나인 사용자 신원을 파악하며 또한 현재 사용자의 위치를 추정하는데 직접적으로 사용이 가능한 방으로의 들어오고 나감 (즉, 출/입 행동)을 감지하는 효과적인 방법을 제안한다. 개발된 시스템은 [1]에 제안되었던 방법을 개선시킨 것으로 초음파 센서 및 PC를 이용하여 만들어졌던 시스템을 8bit 마이크로 컨트롤러를 사용한 임베디드 시스템의 형태로 구현하였다. 이와 더불어 복수개의 센싱 시스템에서 감지한 신호를 블루투스에 기반한 무선 전송 채널을 통해 1개의 중앙 컴퓨터로 전송하는 무선 센서 네트워크를 구성하였다. 이렇게 구성된 센서 네트워크를 통해 각 센싱 모듈이 검출한 사용자 인식 및 인식된 사용자의 출/입 이벤트를 기록, 저장하는 시스템을 구현하였다. 개발된 시스템은 임베디드 시스템의 특성에 적합하도록 기존 PC기반으로 개발된 알고리즘을 수정 개선하였고, 성능 검증을 위해 일반 가정집에 3개의 센싱 모듈을 설치하여 3명의 사용자를 대상으로 실험을 수행하였다.

• Journal of Electrical Engineering and Technology
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• v.9 no.1
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• pp.372-377
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• 2014

Being able to monitor the heart will allow the diagnosis of heart diseases for patients during daily activities, and the detection of burden on the heart during strenuous exercise. Furthermore, with the help of U-health technology, immediate medical action can be taken, in the case of abnormal symptoms of the heart in daily life. Therefore, it appears to be necessary to develop the corresponding technology to monitor the condition of the heart daily. In this study, a novel wearable smart system was proposed, to monitor the activity of the heart in daily life, and to further evaluate the rhythm of arrhythmia. The wearable system includes three modified bipolar conductive fiber electrodes in the chest part, which can resolve the reduction problem of the magnitude of the signal, by magnifying the signal and removing the noise, to obtain high affinity and validity for medical-type usage (<0.903%). The biological signal acquisition and data lines, and the signal processing engine and communication consist of a conductive ink, and the pic18 and ANT protocol nRF24AP2, respectively. The proposed algorithm was able to detect a strong ECG, signal and r-point passing over the noise. The confidence intervals were 96 %, which could satisfy the requirement to detect arrhythmia under the unconstrained conditions.

• The Bulletin of The Korean Astronomical Society
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• v.40 no.1
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• pp.66.2-66.2
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• 2015

In today's age when telecommunications using satellite has become part of our daily lives, one has to be employ preventive measures to avert any possible danger, of which solar activity is the major cause. Coronal mass ejections (CMEs) heading towards the Earth can lead to disturbances in the Earth's magnetosphere, if their magnetic field is oriented southward. Monitoring of solar filaments in this case becomes very very crucial, as their eruption is associated with most of the CMEs. Monitoring of solar filaments in this case becomes very very crucial, as their eruption is associated with most of the CMEs. Also, filaments show activation up to a few hours prior to launch of a CME and thus can provide advance warning. In this study, we present an algorithm for the detection of solar filaments seen in the extreme ultraviolet (EUV) from Atmospheric Imaging Assembly (AIA) on board the Solar Dynamics Observatory (SDO). Various morphological operations are employed to identify and extract the filaments. These filaments are then tracked in order to determine their size and location continuously.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.10a
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• pp.265-269
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• 2008

Home health care with compact wearable units sounds to be a convenient solution for the elderly people living independently. This paper presents a method to detect fall from the other activities of daily living and also to classify those activities. This kind of ambulatory monitoring enables them to get an emergency help in the case of the fatal fall event and can provide their general health status by observing the activities being performed in daily life. A tri-axial accelerometer sensor is used to get the acceleration anomalies associated with the user's movements. The three axis acceleration data are transferred to the base station sensor node via an IEEE 802.15.4 compliant zigbee module. The base station sensor node sends the data to base station PC for an offline processing. This work shows the feature set preparation using the principal component analysis (PCA) for the designing of neural network. The work includes the most common activities of daily living (ADL) like Rest, Walk and Run along with the detection of fall events from ADL. The angle from the vertical is found to be the most significant feature parameter for classification of fall while mean, standard deviation and FFT coefficients were used as the feature parameter for classifying the other activities under consideration. The accuracy for detection of fall events is 86%. The overall accuracy for ADL and fall is 94%.

• Proceedings of the Korean Information Science Society Conference
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• 2008.06b
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• pp.124-129
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• 2008

독거노인의 수가 증가함에 따라 노인의 건강한 생활 패턴 유지 및 응급상황탐지 등을 위해 생활모니터링에 대한 연구가 요구되고 있다. 본 논문에서는 단순히 사물에 대한 접촉만으로 일상생활행위(ADL : activity of daily living)를 인식하기 보다는 노인의 행동과 연관이 있는 사물의 접촉을 함께 고려한 행위인 요소ADL를 인식하여 정확하게 최종 ADL를 인식할 수 있도록 한다. 또한, 행위센서로부터 인식된 물리적 행위분류는 간혹 튀는 데이터들로 인해 잘못된 결과가 나오므로, 이를 보정함으로써 인식의 정확성을 더 보장한다. 실험결과는 8개의 요소ADL에 대해 97% 이상의 인식 결과를 보이며, 이는 최종 ADL을 인식하는데 효율적으로 적용할 수 있음을 보인다.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.3
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• pp.248-253
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• 2008

This paper presents a sensing system for smart home which can detect an location transition events such as entrance/exit of a member and identify the user in a group at the same time. The proposed system is compose of two sub-systems; a wireless sensor network system and a database server system. The wireless sensing system is designed as a star network where each of sensing modules with ultrasonic sensors and a Bluetooth RF module connect to a central receiver called Bluetooth access point. We propose a method to discriminate a user by measuring the height of the user. The differences in the height of users is a key feature for discrimination. At the same time, the each sensing module can recognize whether the user goes into or out a room by using two ultrasonic sensors. The server subsystem is a sort of data logging system which read the detected event from the access point and then write it into a database system. The database system could provide the location transition information to wide range of context-aware applications for smart home easily and conveniently. We evaluate the developed method with experiments for three subjects in a family with the installation of the developed system into a real house.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.249-251
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• 2021

In this paper, we introduce the deep-learning system using Tensorflow for recognizing situations that can occur fall situations when the elderly are moving or standing. Fall detection uses the LSTM (long short-term memory) learned using Tensorflow to determine whether it is a fall or not by data measured from wearable accelerator sensor. Learning is carried out for each of the 7 behavioral patterns consisting of 4 types of activity of daily living (ADL) and 3 types of fall. The learning was conducted using the 3-axis acceleration sensor data. As a result of the test, it was found to be compliant except for the GDSVM(Gravity Differential SVM), and it is expected that better results can be expected if the data is mixed and learned.

• Journal of the Korea Society of Computer and Information
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• v.27 no.2
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• pp.25-32
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• 2022

In this paper, we propose a pet monitoring system based on deep learning using an activity recognition device. The system consists of a pet's activity recognition device, a pet owner's smart device, and a server. Accelerometer and gyroscope data were collected from an Arduino-based activity recognition device, and the number of steps was calculated. The collected data is pre-processed and the amount of activity is measured by recognizing the activity in five types (sitting, standing, lying, walking, running) through a deep learning model that hybridizes CNN and LSTM. Finally, monitoring of changes in the activity, such as daily and weekly briefing charts, is provided on the pet owner's smart device. As a result of the performance evaluation, it was confirmed that specific activity recognition and activity measurement of pets were possible. Abnormal behavior detection of pets and expansion of health care services can be expected through data accumulation in the future.

• Journal of IKEEE
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• v.14 no.1
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• pp.17-24
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• 2010

This paper presents calorie expenditure prediction model of daily activity of elderly living alone for LBS(Location Based Service) applications. The proposed method is to describe the daily activity patterns of older adult using PIR (Passive InfraRed) motion sensors and to examine the relationships between physical activity and calorie expenditure. The developed motion detecting system is composed of a sensing system and a server system. The motion detecting system is a set of wireless sensor nodes which has PIR sensor to detect a motion of elder. Each sensing node sends its detection signal to a home gateway via wireless link. The home gateway stores the received signals into a remote database. The server system is composed of a database server and a web server, which provides web-based monitoring system to caregivers for more effective services. The experiment results show the adaptability and feasibility of the calorie expenditure model.

• Journal of Korea Society of Digital Industry and Information Management
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• v.7 no.1
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• pp.19-29
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• 2011

The u-Healthcare system, a new paradigm, provides healthcare and medical service anytime, anywhere in daily life using wired and wireless networks. It only doesn't reach u-Hospital at home, to manage efficient personal health in fitness space, it is essential to feedback process through measuring and analyzing a personal vital signs. MBAN(Medical Body Area Network) is a core of this technology. MBAN, a new paradigm of the u-Healthcare system, can provide healthcare and medical service anytime, anywhere on real time in daily life using u-sensor networks. In this paper, an ontology-based context-awareness in MBAN proposed system development methodology. Accordingly, ontology-based context awareness system on MBAN to Elderly/severe patients/aged/, with measured respiratory rate/temperature/pulse and vital signs having small variables through u-sensor network in real-time, discovered abnormal signs and emergency situations which may happen to people at sleep or activity, alarmed and connected with members of a family or medical emergency alarm(Emergency Call) and 119 system to avoid sudden accidents for early detection. Therefore, We have proposed that accuracy of biological signal sensing and the confidence of ontology should be inspected.