• Steel and Composite Structures
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• 제35권6호
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• pp.779-788
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• 2020

Multi-Walled Carbon nanotubes (MWCNT) coupled with Silicone Rubber (SR) can represent applicable strain sensors with accessible materials, which result in good stretchability and great sensitivity. Employing these materials and given the fact that the combination of these two has been addressed in few studies, this study is trying to represent a low-cost, durable and stretchable strain sensor that can perform excellently in a high number of repeated cycles. Great stability was observed during the cyclic test after 2000 cycles. Ultrahigh sensitivity (GF>1227) along with good extensibility (ε>120%) was observed while testing the sensor at different strain rates and the various number of cycles. Further investigation is dedicated to sensor performance in the detection of human body movements. Not only the sensor performance in detecting the small strains like the vibrations on the throat was tested, but also the larger strains as observed in extension/bending of the muscle joints like knee were monitored and recorded. Bearing in mind the applicability and low-cost features, this sensor may become promising in skin-mountable devices to detect the human body motions.

• 전기학회논문지
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• 제63권7호
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• pp.940-944
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• 2014

In this research, we designed representative motion patterns that possibly occurred in sleep situation and evaluated the feasibility of the smartphone based movement recording technique. For this, we designed 7 motions such as posture change, head movement, arm movement (vertical, horizontal), leg movement and hand movement (flipping, folding). Movement was recorded by using the smartphone and the actimetry device simultaneously for comparing the feasibility of smartphone based recording. As a result of experiment, we found that the smartphone based movement recording well reflects the body movement, however, it shows the limitation in recording the small local movement such as hand motion compared with the reference actimetry device, Actiwatch.

• Asian-Australasian Journal of Animal Sciences
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• 제29권1호
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• pp.149-156
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• 2016

In this experiment, we proposed and implemented a disease forecasting system using a received signal strength indication ZigBee-based wireless network with a 3-axis acceleration sensor to detect illness at an early stage by monitoring movement of experimentally infected weaned piglets. Twenty seven piglets were divided into control, Salmonella enteritidis (SE) infection, and Escherichia coli (EC) infection group, and their movements were monitored for five days using wireless sensor nodes on their backs. Data generated showed the 3-axis movement of piglets (X-axis: left and right direction, Y-axis: anteroposterior direction, and Z-axis: up and down direction) at five different time periods. Piglets in both infected groups had lower weight gain and feed intake, as well as higher feed conversion ratios than the control group (p<0.05). Infection with SE and EC resulted in reduced body temperature of the piglets at day 2, 4, and 5 (p<0.05). The early morning X-axis movement did not differ between groups; however, the Y-axis movement was higher in the EC group (day 1 and 2), and the Z-axis movement was higher in the EC (day 1) and SE group (day 4) during different experimental periods (p<0.05). The morning X and Y-axis movement did not differ between treatment groups. However, the Z-axis movement was higher in both infected groups at day 1 and lower at day 4 compared to the control (p<0.05). The midday X-axis movement was significantly lower in both infected groups (day 4 and 5) compared to the control (p<0.05), whereas the Y-axis movement did not differ. The Z-axis movement was highest in the SE group at day 1 and 2 and lower at day 4 and 5 (p<0.05). Evening X-axis movement was highest in the control group throughout the experimental period. During day 1 and 2, the Z-axis movement was higher in both of the infected groups; whereas it was lower in the SE group during day 3 and 4 (p<0.05). During day 1 and 2, the night X-axis movement was lower and the Z-axis movement was higher in the infected piglets (p<0.05). Overall, the movement of infected piglets was altered, and the acceleration sensor could be successfully employed for monitoring pig activity.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2003년도 학술회의 논문집 정보 및 제어부문 B
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• pp.920-923
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• 2003

An wearable electrocardiogram (ECG) monitoring system is a widely used non-invasive diagnostic tool for ambulatory patient who may be at risk from latent life-threatening cardiac abnormalities. In this paper, we have a portable ECG monitoring system with conductive fiber which was characterized by the small-size and the low power consumption. The system consists of conductive fibers, one-chip microcontroller, ECG preprocessing circuit, and monitoring software to be able to record and analyze in PC. ECG preprocessing circuit is made of pre-amplifier with gain of 10, band-pass filter with bandwidth of 0.5-120Hz and 2.5V offset circuit for A/D conversion. ECG signals obtained by sensor are included with corrupted noises such as a baseline wandering, 60 Hz power noise and interference noise by body movement. For cancellation corrupted noises in signals obtained by conductive fiber, we used the wavelet decomposition of wavelet transforms in MATLAB toolbox.

• 한국정보통신학회논문지
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• 제20권6호
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• pp.1189-1194
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• 2016

삶의 편의성이 증대되면서 체질량 지수 (Body Mass Index)가 높은 인구가 급속도로 증가하고 있다. 또한 이에 따라 비만을 관리하고 예방하기 위한 모니터링 시스템의 개발이 요구되고 있는 시점이다. 그러나 대부분의 모니터링 시스템은 사용자가 관리를 받기에는 정보가 적고, 간단한 정보만을 가지고 계산만하여 보여주기 때문에 효율성이 낮고, 질병을 가진 사용자를 정상인과 함께 관리하여 정확도가 떨어졌다. 이에 본 논문에서는 사용자의 체질량 지수를 그래프로 표현하고, BMR(Basal metabolic rate)지수를 등급으로 나누어 사용자가 자신을 관리하는데 정확도를 높였다. 또한 사용자가 사용한 운동 기구를 사용한 회수 별로 나타내 균형 잡힌 운동 기구 선택을 할 수 있게 하였고, 소모한 칼로리를 같이 나타내 운동 계획을 설계하는데 효율성을 높였고, 그래프의 경우 최근 데이터만 나타내 시각성을 높인 웹 기반 모니터링 시스템을 개발하였다.

• 대한의용생체공학회:의공학회지
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• 제26권5호
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• pp.331-335
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• 2005

The fusion technology of small sensor and wireless communication was followed by various application examples of the embedded system, where the social infrastructural facilities and ecological environment were wirelessly monitored. In addition, this technology represents the primary application area being extended into the healthcare field. In this study, a body area network for ubiquitous healthcare is presented. More specifically this represents a wireless biomedical signal acquisition device characterized by small size, low power consumption, pre-processing and archiving capability. Using this device, a new method for monitoring vital signs and activity is created. A PDA-based wireless sensor network enables patients to be monitored during their daily living, without any constraints. Therefore, the proposed method can be used to develop Activities of Daily Living (ADL) monitoring devices for the elderly or movement impaired people. A medical center would be able to remotely monitor the current state of elderly people and support first-aid in emergency cases. In addition, this method will reduce medical costs in society, where the average life expectancy is increasing.

• 정보처리학회논문지B
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• 제9B권5호
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• pp.663-672
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• 2002

본 논문에서는 자동 추적 시스템을 이용하여 카바메이트 계열의 농약인 카보퓨란의 치명적인 투여에 대하여 반자연적인 조건에서 반응하는 깔따구의 움직임을 관찰하였다. 4령기에 있는 깔따구를 $6cm\times{7cm}\times{2.5cm}$ 크기의 서식 장소와 $18^\circ{C}$의 수온, 명기와 암기를 각각 10시간, 14시간의 조건에서 관찰을 하였다. 추적 시스템은 깔따구 몸체의 부분 점들을 탐지하여 추적하도록 하였다. 모든 실험은 반자연적인(semi-natural) 상태에서 진행되었으며 약제 카보퓨란(Carbofuran 0.1mg/l) 처리 전 후 이틀씩 모두 4일에 걸쳐서 연속적으로 진행되었다. 실험 결과 약제의 처리후에 압축된 지그제그 형태로 나타나는 "떨림 현상"과 같은 비정규적인 행동들이 종종 나타남을 알 수 있었다. 약제 처리된 종들의 행동 변화를 탐지하기 위하여, 웨이블릿 분석이 다른 움직임 패턴들을 특징화하기 위하여 사용되었다. 이산 웨이블릿에 기반하여 추출된 파라미터들은 약제처리 전후의 움직임에 대한 다른 유형의 패턴들을 표현하기 위하여 인공 신경망을 통하여 학습되었다. 이러한 웨이블릿과 인공 신경망의 통합 모델은 특징화된 움직임 패턴들의 발생 시점을 탐지할 수 있었으며, 수질 모니터링을 위한 독성 물질의 유입을 자동으로 탐지할 수 있는 도구로써 사용될 수 있음을 알 수 있었다.을 알 수 있었다.

• 한국위성정보통신학회논문지
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• 제8권2호
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• pp.17-21
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• 2013

빌딩 등 건물에너지 절약에 대한 관심이 전 세게적으로 날로 증가하고 있으며, BEMS(Building Energy Management System)을 효율적으로 운용하기 위한 IT 기술에 대한 연구를 지속적으로 진행하고 있다. 최근 LED 조명기술의 발전으로 LED를 제어하여 에너지 절감효과를 극대화 할 수 있으며 BEMS에 이러한 LED 조명 제어기술들이 개발되고 있다. 본 논문에서는 건물에 설치되어 있는 LED조명과 연동되어 있는 객체감지 센서를 ZigBee 통신을 활용하여 서버와 연결이 되며, 사용자가 서버와 연결된 PC를 통해 모니터링 및 제어가 가능한 시스템을 제안하였다. 설계한 구조를 기반으로 빌딩에서 객체를 감지하여 LED 조명을 실시간으로 모니터링 및 제어 가능한 기능을 제안하고, 관련 Software 개발을 통해 구현가능성을 입증하였다.

• International Journal of Internet, Broadcasting and Communication
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• 제14권2호
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• pp.119-128
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• 2022

Recently, due to the development of related technologies for autonomous vehicles, driving work is changing more safely. However, the development of support technologies for level 5 full autonomous driving is still insufficient. That is, even in the case of an autonomous vehicle, the driver needs to drive through forward attention while driving. In this paper, we propose a method to monitor driving tasks by recognizing driver behavior. The proposed method uses pre-trained deep convolutional neural network models to recognize whether the driver's face or body has unnecessary movement. The use of pre-trained Deep Convolitional Neural Network (DCNN) models enables high accuracy in relatively short time, and has the advantage of overcoming limitations in collecting a small number of driver behavior learning data. The proposed method can be applied to an intelligent vehicle safety driving support system, such as driver drowsy driving detection and abnormal driving detection.

• International Journal of Contents
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• 제16권3호
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• pp.1-17
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• 2020

Human motion recognition is essential for user-centric services such as surveillance-based security, elderly condition monitoring, exercise tracking, daily calories expend analysis, etc. It is typically based on the movement data analysis such as the acceleration and angular velocity of a target user. The existing motion recognition studies are only intended to measure the basic information (e.g., user's stride, number of steps, speed) or to recognize single motion (e.g., sitting, running, walking). Thus, a new mechanism is required to identify the transition of single motions for assessing a user's consecutive motion more accurately as well as recognizing the user's body and surrounding situations arising from the motion. Thus, in this paper, we collect the human movement data through Android smartphones in real time for five targeting single motions and propose a mechanism to recognize a consecutive motion including transitions among various motions and an occurred situation, with the state transition model to check if a vulnerable (life-threatening) condition, especially for the elderly, has occurred or not. Through implementation and experiments, we demonstrate that the proposed mechanism recognizes a consecutive motion and a user's situation accurately and quickly. As a result of the recognition experiment about mix sequence likened to daily motion, the proposed adoptive weighting method showed 4% (Holding time=15 sec), 88% (30 sec), 6.5% (60 sec) improvements compared to static method.