• Journal of the Korea Society of Computer and Information
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• v.24 no.10
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• pp.33-39
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• 2019

In this paper, we propose a parametric model for analyzing the motion information obtained from the acceleration sensors to measure the activity of the human body. The motion of the upper body and the lower body does not occur at the same time, and the motion analysis method using a single motion sensor involves a lot of errors. In this study, the 3-axis accelerometer is attached to the arms and legs, the body's activity data are measured, the momentum of the arms and legs are calculated for each channel, and the linear predictive coefficient is obtained for each channel. The periodicity of the upper body and the lower body is determined by analyzing the correlation between the channels. The linear predictive coefficient and the periodic value are used as data to measure the type of exercise and the amount of exercise. In the proposed method, we measured four types of movements such as walking, stair climbing, slow hill climbing, and fast hill descending. In order to verify the usefulness of the parameters, the recognition results are presented using the linear predictive coefficient and the periodic value for each motion as the neural network input.

• Journal of radiological science and technology
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• v.27 no.3
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• pp.19-24
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• 2004

MRI imaging is one of the imaging techniques showing anatomical structures of human body for medical diagnosis, and has been researched in order to provide better quality of anatomical information. In this study, we propose a very useful method to extract an interest areas and how to diagnose necrolysis of femoral neck disease automatically. Regions of femoral neck is set using anatomical features and Hough transform and advantages of both region extension and histogram-based region segmentation method are combined for better region segmentation. As a result of the proposed method, good imaging quality was obtained for femoral neck with both normal and severe necrosis as well as for femoral neck in early stage of necrolysis.

• Journal of Physiology & Pathology in Korean Medicine
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• v.27 no.6
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• pp.832-841
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• 2013

This research, as a basic preliminary study for development of functional health food, is aimed at assessing the body fat reduction effect and for application to human body for such reduction in actual clinical settings by preliminary extraction of 2 types of wild edible greens, Lythrum salicaria L. and Aceriphyllum rossii. Subjects over the age of 19 and less than 60 years old with BMI value range of $23.0kg/m^2{\sim}29.9kg/m^2$ were recruited through screening were divided into experimental group and control group, each with 25 subjects, through randomized allocation. With both patients and evaluators wearing blindfold, the experimental group was orally administered with 4 capsules of 500mg of composite preparation containing the extracts of Lythrum salicaria L. and Aceriphyllum rossii 3 times a day for a period of 8 weeks while the control group was orally administered with 4 capsules of 500mg of placebo (fake food) with the same appearance as the preparation administered to the experimental group 3 times a day for a period of 8 weeks. After having carried out evaluation on physical examinations (body weight, BMI and body fat ratio, etc.), laboratory tests (general blood test, biochemical test of blood and urine test), lipid test, the changes were analyzed. There was no significant change between the 2 groups and within the groups in BMI and body fat ratio, which are the primary effectiveness evaluation at each time. There was no significant difference between the 2 groups in serum lipid and WHR, obesity related KOQOL, KEAT-26 which are the secondary effectiveness evaluations. There was no change between the 2 groups and within the groups in vital sign, CBC, BC and urin test. These results suggest that Lythrum salicaria L. and Aceriphyllum rossii ext. showed no significant reduction in BMI, body fat ratio and serum lipid. Additional confirmative clinical application test is needed in the future.

• Journal of Korea Society of Industrial Information Systems
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• v.4 no.3
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• pp.21-27
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• 1999

This paper introduces the research and development of automatic generation technology to develop the character agent. The R&D of this technology includes three major elements-body model generation, automatic motion generation and synthetic human generation. Main areas of application would by cyber space- 3D game, animation, virtual shopping, on line chatting, virtual education system, simulation and security system.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.12 no.3
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• pp.89-95
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• 2013

This study focused on the development on functional shoe apparatus so that the appropriate impact can be applied to the feet in order to improve the density of mineral bone at lower limbs. The model with structure proposed in this study had an effective stress about 20 to 100% higher by comparing that of the model without it among most of 15 bone extraction points. Though there is a limitation that the finite element analysis data from the human body model are not the value of mineral bone densities by measuring directly but the effective bone stresses against impact, the proposed structure is designed to influence the increase of bone mass and improve the density of mineral bone by effecting the improvement of the density of mineral bone actually.

• Journal of Computing Science and Engineering
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• v.10 no.4
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• pp.128-136
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• 2016

It is impossible for any human being to analyze the more than 500 million tweets that are generated per day. Lexical ambiguities on Twitter make it difficult to retrieve the desired data and relevant topics. Most of the solutions for the word sense disambiguation problem rely on knowledge base systems. Unfortunately, it is expensive and time-consuming to manually create a knowledge base system, resulting in a knowledge acquisition bottleneck. To solve the knowledge-acquisition bottleneck, a topic signature is used to disambiguate words. In this paper, we evaluate the effectiveness of various features of newspapers on the topic signature extraction for word sense discrimination in tweets. Based on our results, topic signatures obtained from a snippet feature exhibit higher accuracy in discriminating company names than those from the article body. We conclude that topic signatures extracted from news articles improve the accuracy of word sense discrimination in the automated analysis of tweets.

• Proceedings of the KIEE Conference
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• 2006.07d
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• pp.2081-2082
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• 2006

본 논문에서는 지능형 로봇 시스템에서 신경 회로망을 이용한 인간 몸의 제스처 추출 기법을 제안 하였다. 지능형 로봇 시스템에서 사용된 컴퓨터 시각 기반에서는 시간상의 변화에 따른 특징 벡터 추출을 필요로 한다. 이를 위해 본 논문에서는 신경 회로망을 이용한 제스처 추출 기법을 제안 하였다. 신경 회로망을 이용한 제스처 추출은 오류 역 전파 학습방법을 사용하여 시간상에서 변화하는 영상 시퀀스에 정보를 생성하고 움직임 모델을 통해 두 정보간의 따른 제스처 추출에 가중치를 준다. 마지막으로 본 연구에서 제안한 기법은 실험을 통해 그 우수성을 확인하였다.

• YAKHAK HOEJI
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• v.36 no.3
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• pp.241-249
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• 1992

The whole body extract of Lunella coronata coreensis agglutinated nonspecifically human and other animal erythrocytes. A new lectin was purified by the following procedures: 0.15 M NaCl extraction, salt fractionation, gel filtration, anionic and cationic ion exchange column chromatographies. Through these purification procedures, specific activity of LCC-I was increased from 276 to 9714.3 units/mg, And on polyacrylamide gel electrophoresis, LCC-I exhibited one major band. A molecular weight of LCC-I was assumed to be 20,000 by sodium dodesyl sulfate polyacrylamide gel electrophoresis. The purified lectin was relatively stable at various pH and heat. Among the tested sugars, lactose and lactulose inhibited lectin activity at a concentration of 6.25 mM, respectively.

• Proceedings of the Korea Information Processing Society Conference
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• 2000.10a
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• pp.179-182
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• 2000

본 논문은 특별한 마커를 사용하지 않고 연속되는 영상들에서 사람의 2 차원 움직임 정보를 추출하는 알고리즘을 제안한다. 사람의 움직임 정보를 추출하기 위해 색상, 움직임, 그리고 윤곽선 정보를 이용한다. 뿐만 아니라 사용자의 신체적인 차이와 특징점의 일관성을 위해 사람 몸통 모델을 사용한다. 본 논문의 알고리즘은 마커를 사용할 수 없는 HCI 응용분야에 될 수 있다.

• Journal of Sensor Science and Technology
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• v.29 no.3
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• pp.162-171
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• 2020

The incidences of cardiovascular diseases are rapidly increasing worldwide. The electrocardiogram (ECG) is a test to detect and monitor heart issues via electric signals in the heart. Presently, detecting heart disease in real time is not only possible but also easy using the myDAQ data acquisition device and LabVIEW. Hence, this paper proposes a system that can acquire ECG signals in real time, as well as detect heart abnormalities, and through light-emitting diodes (LEDs) it can simultaneously reveal whether a particular waveform is in range or otherwise. The main hardware components used in the system are the myDAQ device, Vernier adapter, and ECG sensor, which are connected to ECG monitoring electrodes for data acquisition from the human body, while further processing is accomplished using the LabVIEW software. In the Results section, the proposed system is compared with some other studies based on the features detected. This system is tested on 10 randomly selected people, and the results are presented in the Simulation Results section.