• 한국임상수의학회지
• /
• 제35권3호
• /
• pp.97-99
• /
• 2018

The aim of this study is to determine whether subcutaneous temperature (ST) was correlated with rectal temperature (RT) in cattle with inducing artificial fever. In order to determine the correlation between their temperatures, the experiment was performed as follow: Among nine Holstein steers, lipopolysaccharide (LPS) was intravenously administered at a dose of $0.5{\mu}g/kg$ of body weight to six Holstein steer, then, 6 ml of saline was administrated to three steers as a control group. After LPS injection, ST was recorded using subcutaneously implanted thermo-logger sensors at 10-min intervals, and RT was measured using a digital thermometer at 0, 1, 2, 3, 4, 8 and 12 h. In steers with LPS injection, RT was highest at 3 to 4 h and recovered to a pre-challenge temperature at 8-22 h. A similar fluctuation was shown in ST except for an unexpected decrease at 1 h, and a positive correlation between RT and ST was observed in LPS-challenged steers (r = 0.497, P = 0.04). This result suggests that ST could be utilized as an index for early detection of infectious diseases or physiological events.

• 한국지능시스템학회논문지
• /
• 제18권1호
• /
• pp.72-77
• /
• 2008

개인 식별 연구는 보안, 감시 시스템에서 중요한 부분이다. 최선의 성능을 가진 시스템을 설계하기 위하여 감지기들로부터 최대 정보를 이용할 수 있도록 설계한다. 다양한 생체 인식 시스템은 등록, 확인, 또는 개인 식별을 위하여 생리 특성이나 행동 특성을 하나이상 활용한다. 발걸음 인식만을 가지고는 아직 개인별 변별적 특징을 안정적으로 나타내지 못하므로, 본 논문에서는 얼굴과 발걸음을 결합한 개인 식별 시스템을 제안한다. 본 논문에서 우리는 한 개의 카메라를 이용한다. 즉, 얼굴과 발걸음 인식 모두 하나의 카메라를 이용하여 획득된 같은 이미지 셋을 사용한다. 본 논문의 중점은 이미지들에서 이용할 수 있는 최대 정보량을 활용하는 것으로 시스템의 성능을 향상시키는 것이다. 결합은 결정 단계에서 고려된다. 제안된 알고리듬은 NLPR 데이터베이스를 사용한다.

• 한국농촌생활과학회지
• /
• 제9권1호
• /
• pp.1-7
• /
• 1998

The purpose of this study is to investigate the effect of wearing sweat suit on sweating rate during jogging. 4 healthy female students served as subjects in the experimental chamber which was controlled 22$\pm$1$^{\circ}C$, 60$\pm$10%RH and no wind. The experimental clothes were Sweat Suit (SS) and General Suit (GE), SS was the product of R sports wear company which was consisted of long-sleeved jumper (100% polyester) and full length trousers (100% polyester) and GE were consisted of long sleeved shirt (100% cotton) and full length trousers (100% cotton). The subject wore same socks and shoes in both experimental clothes SS and GE. The subject reported at the experimental chamber at the same time on each experimental day. exchanged their clothes to the experimental clothes SS or GE, wore all sensors for the physiological measurements and had a rest in a sitting posture about 40 minutes. After rest, the subject carried out 30 min jogging on the tread mill with the speed 3.6miles/hour and during the jogging rectal temperature, skin temperatures (7 sites of the skin surface), heart rate, VO2, and evaporative weight loss were measured continuously and compared between two experimental clothes SS and GE. The major findings were as follows : The increase in rectal temperature during 30 min jogging was higher in experimental clothes SS than in GE and mean slim temperature kept higher in SS than in GE. VO2 and heart rate were a little bit higher in the later period of jogging in SS than in GE. The evaporative weight loss was greater in SS than in GE. These results indicate that the thermophysiological responses and sweating rate differs according to the wearing suit even though the subject performed same exercise.

• 융합정보논문지
• /
• 제7권5호
• /
• pp.129-135
• /
• 2017

본 논문에서는 스마트폰과 생체센서를 사용하여 몸이 불편한 고령자의 상태를 수시로 모니터링하고 보호자 및 간병인으로 하여금 언제, 어디서나 최고의 의료서비스를 제공할 수 있도록 하는 시스템을 제안한다. 제안 시스템은 아두이노를 기반으로 병실에 설치된 다양한 생체센서들을 통해 고령자의 상태를 모니터링하고 고령자가 필요로 하는 생리적, 의료적 서비스를 제공할 수 있게 할뿐 아니라, 만일의 경우 보호자에게 알려 위급한 순간을 대처할 수 있게 하였다. 결론적으로, 본 논문은 아두이노와 안드로이드 애플리케이션(앱)을 이용하여 고령자가 사용하는 독서 등을 생체센서가 달린 홈 서버로 동작하게 하고, 간병인 및 보호자의 스마트폰을 원격관리 및 긴급호출 시스템으로 구성함으로써 향후 만성질환 고령자와 간병인 모두에게 의료서비스 만족도를 향상 시키는 중요한 방안을 제시한다.

• 센서학회지
• /
• 제20권2호
• /
• pp.137-144
• /
• 2011

Wireless sensor network(WSN) has the potential to greatly effect many aspects of u-healthcare. By outfitting the potential with WSN, wearable sensor node can collects real-time data on physiological status and transmits through base station to server PC. However, there is a significant gap between WSN and healthcare. WSN has the limited resource about computing capability and data transmission according to bio-sensor sampling rates and channels to apply healthcare system. If a wearable node transmits ECG and accelerometer data of 4 channel sampled at 100 Hz, these data may occur high loss packets for transmitting human activity and ECG to server PC. Therefore current wearable sensor nodes have to solve above mentioned problems to be suited for u-healthcare system. Most WSN based activity and ECG monitoring system have been implemented some algorithms which are applied for signal vector magnitude(SVM) algorithm and ECG noise algorithm in server PC. In this paper, A wearable sensor node using integrated ECG and 3-axial accelerometer based on wireless sensor network is designed and developed. It can form multi-hop network with relay nodes to extend network range in WSN. Our wearable nodes can transmit 1-channel activity data processed activity classification data vector using SVM algorithm to 3-channel accelerometer data. ECG signals are contaminated with high frequency noise such as power line interference and muscle artifact. Our wearable sensor nodes can remove high frequency noise to clear original ECG signal for healthcare monitoring.

• 센서학회지
• /
• 제15권4호
• /
• pp.237-244
• /
• 2006

Atomic force microscope (AFM) has become a common tool for the structural and physical studies of biological macromolecules, mainly because it provides the ability to perform experiments with samples in a buffer solution. In this study, structure of proteins and nucleic acids has been studied in their physiological environment that allows native intermolecular complexes to be formed. Cr and Au were deposited on p-Si (100) substrate by thermal evaporation method in sequence with the thickness of $200{\AA}$ and $500{\AA}$, respectively, since Au is adequate for immobilizing biomolecules by forming a self-assembled monolayer (SAM) with semiconductor-based biosensors. The SAM, streptavidin and biotin interacted each other with their specific binding energy and their adsorption was analyzed using the Bio-AFM both in a solution and under air environment. A silicon nitride tip was used as a contact tip of Bio-AFM measurement in a solution and an antimony doped silicon tip as a tapping tip under air environment. Actual morphology could also be obtained by 3-dimensional AFM images. The length and agglomerate size of biomolecules was measured in stages. Furthermore, $R_{a}$ (average of surface roughness) and $R_{ms}$ (mean square of surface roughness) and surface density for the adsorbed surface were also calculated from the AFM image.

• 대한건축학회논문집:계획계
• /
• 제34권11호
• /
• pp.75-84
• /
• 2018

This study analyzed users' acceptance and intention to use in addition to needs and preferences of smart home technologies, and identified the differences in technology preference and acceptance by different factors. The subjects were residents in the 40s and 60s residing in the Seoul or suburbs of Seoul, and questionnaires were conducted in the 40s while interviews with questionnaires were conducted in the 60s. A total of 105 questionnaires were used as data, and frequency, mean, crossover, independent sample t test, one-way ANOVA and multiple regression analysis were performaed using SPSS23. The results of this study are as follows. First, hypertension, hyperlipidemia and hypercholesterolemia were the most common diseases among respondents and if there was no discomfort, they would like to continue living in the homes of the current residence. Therefore, the direction of smart home development should support the daily living and health care so that residents can live a healthy life for a long time in their living space. Second, the technologies that residents most need were a control technology of residential environments and a monitoring technology of residents' health and physiological changes. The most preferred sensor types are motion sensors and speech recognition while video cameras have a very low preference. Third, technology anxiety was the most significant factor influencing intention to accept smart home technology. The greater the technology anxiety is, the weaker the acceptance of technology. Fourth, when applying smart residential technology in homes, various resident characteristics should be considered. Age and technology intimacy were the most influential variables, and accordingly there were differences in technology preference and acceptance. Therefore, a user-friendly smart home plan should be done in the consideration of the results.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제15권7호
• /
• pp.2304-2320
• /
• 2021

Intelligently detecting anomalies in health sensor data streams (e.g., Electrocardiogram, ECG) can improve the development of E-health industry. The physiological signals of patients are collected through sensors. Timely diagnosis and treatment save medical resources, promote physical health, and reduce complications. However, it is difficult to automatically classify the ECG data, as the features of ECGs are difficult to extract. And the volume of labeled ECG data is limited, which affects the classification performance. In this paper, we propose a Generative Adversarial Network (GAN)-based deep learning framework (called CAB) for heart arrhythmia classification. CAB focuses on improving the detection accuracy based on a small number of labeled samples. It is trained based on the class-imbalance ECG data. Augmenting ECG data by a GAN model eliminates the impact of data scarcity. After data augmentation, CAB classifies the ECG data by using a Bidirectional Long Short Term Memory Recurrent Neural Network (Bi-LSTM). Experiment results show a better performance of CAB compared with state-of-the-art methods. The overall classification accuracy of CAB is 99.71%. The F1-scores of classifying Normal beats (N), Supraventricular ectopic beats (S), Ventricular ectopic beats (V), Fusion beats (F) and Unclassifiable beats (Q) heartbeats are 99.86%, 97.66%, 99.05%, 98.57% and 99.88%, respectively. Unclassifiable beats (Q) heartbeats are 99.86%, 97.66%, 99.05%, 98.57% and 99.88%, respectively.

• 한국전자통신학회논문지
• /
• 제17권3호
• /
• pp.491-498
• /
• 2022

우리의 일상생활에서 양질의 수면은 행복 지수와 밀접한 관계가 있다. 사람들은 수면 장애를 만성 질환으로 인식하든 아니든 많은 어려움을 호소하고 있으며 일상생활에서 수면 중에 호흡 곤란을 경험하는 경우가 종종 발생한다. 수면 중에 호흡 관련 장애를 자동으로 인식하는 것은 매우 중요하나 현실적으로 매우 어렵다. 본 논문은 이러한 문제를 해결하기 위해 가정에서 건강관리를 위해 모바일 기반의 비접촉 수면 모니터링을 제안한다. 수면 중 호흡 신호는 스마트 폰의 소리 센서를 이용하여 호흡 신호를 수집하고, 신호의 특징을 추출, 호흡의 주파수, 진폭, 호흡의 주기, 호흡의 패턴을 분석한다. 모바일 건강이 모든 문제를 해결하지는 못하나 개인의 건강 상태의 조기 발견과 지속적인 관리를 목적으로 하고, 일반 가정의 침실에서 스마트 폰으로 추가 센서 없이 수면 중에 호흡과 같은 생리학적 데이터 모니터링의 가능성을 보여준다.

• 한국정보통신학회:학술대회논문집
• /
• 한국정보통신학회 2021년도 춘계학술대회
• /
• pp.240-243
• /
• 2021

최근에 빅 데이터와 AI 기술을 교육의 평가와 개별 학습에 적용하는 연구 성과가 있었다. 정보 기술의 혁신으로 소셜 미디어, MOOC, 지능형 개인지도 시스템, LMS, 센서 및 모바일 장치 등으로부터 학생들의 개인 기록, 생리학적 데이터, 학습 로그 및 활동, 학습 성과 및 결과를 포함하는 동적이고 복잡한 데이터를 수집 가능하였다. 또한 COVID-19 환경에서 e-러닝이 활성화 되어 많은 양의 학습 데이터가 생성되었다. 이 데이터로부터 학습 분석과 AI 기술을 적용하여 의미있는 패턴의 추출과 지식의 발견이 될 것으로 예상된다. 학습자 측면에서 학생의 학습 및 정서적 행동 패턴과 프로필을 식별하고, 평가 및 평가 방법을 개선하고, 개별 학생의 학습 성과 또는 중퇴를 예측하고, 개인화 된 지원을 위한 적응 시스템에 대한 연구는 필요하다. 본 연구에서는 교육용 데이터를 대상으로 이상탐지와 추천시스템에서 사용하는 기계학습 기술에 대한 조사와 분류를 하여 교육 분야의 연구에 기여하고자 한다.