• Journal of Korea Multimedia Society
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• v.21 no.12
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• pp.1473-1480
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• 2018

The extraction and recognition of human motion characteristics need to combine biometrics to determine and judge human behavior in the movement and distinguish individual identities. The so-called biometric technology, the specific operation is the use of the body's inherent biological characteristics of individual identity authentication, the most noteworthy feature is the invariance and uniqueness. In the past, the behavior recognition technology based on the single characteristic was too restrictive, in this paper, we proposed a mixed feature which combined global silhouette feature and local optical flow feature, and this combined representation was used for human action recognition. And we will use the KTH database to train and test the recognition system. Experiments have been very desirable results.

• Journal of KIISE:Software and Applications
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• v.32 no.8
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• pp.759-768
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• 2005

This paper describes an algorithm that can recognize gestures constructing subspace gesture symbols with hybrid feature information. The previous popular methods based on geometric feature and appearance have resulted in ambiguous output in case of recognizing between similar gesture because they use just the Position information of the hands, feet or bodily shape features. However, our proposed method can classify not only recognition of motion but also similar gestures by the partial feature information presenting which parts of body move and the global feature information including 2-dimensional bodily motion. And this method which is a simple and robust recognition algorithm can be applied in various application such surveillance system and intelligent interface systems.

• Fashion & Textile Research Journal
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• v.10 no.1
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• pp.92-100
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• 2008

When designing protective clothing, there are something to be considered such as physiological feature of human body, acting range not to restrict physical activity, and effectiveness of material. Because the primary objective of protective clothing is to protect human body from danger and it is designed through complex designing process not likely general clothing design. However, current evaluation techniques-such as the ISO, the ASTM and the CEN, and KS-provide only the standard to evaluate the primary feature of material (testing, performance requirements, material specification, selection and application, test and care, and so on). There are no standard to evaluate influence for the human body while protective clothing put on. Especially, in Korea, there is KS to evaluate protective clothing, but it is partially translated version from ISO because of lack of core technology about this field. However, developed countries recognize it is new competitive means in the time of Global Standards and they are competing to make their own standard to global standard for the protective clothing. Therefore, it can be great opportunity for Korean clothing and textile industry to revitalize if focusing on research and development for protective clothing design based on physical activity of human body, fit evaluation technique and sizing which is currently no global standard for it and developing our standard to global standard.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.5
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• pp.1856-1869
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• 2015

This paper addresses the issues of 3D human activity detection, tracking and recognition from RGB-D video sequences using a feature structured framework. During human tracking and activity recognition, initially, dense depth images are captured using depth camera. In order to track human silhouettes, we considered spatial/temporal continuity, constraints of human motion information and compute centroids of each activity based on chain coding mechanism and centroids point extraction. In body skin joints features, we estimate human body skin color to identify human body parts (i.e., head, hands, and feet) likely to extract joint points information. These joints points are further processed as feature extraction process including distance position features and centroid distance features. Lastly, self-organized maps are used to recognize different activities. Experimental results demonstrate that the proposed method is reliable and efficient in recognizing human poses at different realistic scenes. The proposed system should be applicable to different consumer application systems such as healthcare system, video surveillance system and indoor monitoring systems which track and recognize different activities of multiple users.

• Fashion & Textile Research Journal
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• v.11 no.5
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• pp.788-798
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• 2009

This study aims to develop a highly reproducible, optimal frame design algorithm using variations in the curvature of armscye circumference, which will provide the basics for remodeling the 3D human body shape with the concept of reverse design used to develop total contents for the apparel industry. 1. The results of the experiment proved that ratio value was significantly efficient than absolute value of curvature variation to extract feature points in the armscye circumference 2. For the shoulder(1st and 2nd quadrant) and front armhole(3rd quadrant) parts of the armscye circumference, frame remodeling with the positive point of inflection led to the completion of a highly reproducible frame. 3. Similarly, even for the rear armhole part(4th quadrant) in the armscye circumference, it was found that frame remodeling using the positive maximum point of inflection resulted in highly reproducible body shape with the maximum point of inflection situated within the range of split angles $305^{\circ}{\sim}330^{\circ}$, while frame remodeling using simultaneously the two largest points of inflection including maximum point of inflection led to highly reproducible body shape with the maximum point of inflection out of the range $305^{\circ}{\sim}330^{\circ}$. 4. Based upon the optimal frame design algorithm developed in this study, section-specific feature points in the armscye circumference were extracted depending on the rate of curvature variation and remodeling with spline curves was conducted. The results indicate a remarkably high reproducibility(98.6%) and suggest that the algorithm developed in this study is suitable for human body modeling.

• The Journal of the Convergence on Culture Technology
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• v.5 no.4
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• pp.413-420
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• 2019

The purpose of the present study is to develop a model for predicting hypercholesterolemia using an integrated set of body fat mass variables based on machine learning techniques, beyond the study of the association between body fat mass and hypercholesterolemia. For this study, a total of six models were created using two variable subset selection methods and machine learning algorithms based on the Korea National Health and Nutrition Examination Survey (KNHANES) data. Among the various body fat mass variables, we found that trunk fat mass was the best variable for predicting hypercholesterolemia. Furthermore, we obtained the area under the receiver operating characteristic curve value of 0.739 and the Matthews correlation coefficient value of 0.36 in the model using the correlation-based feature subset selection and naive Bayes algorithm. Our findings are expected to be used as important information in the field of disease prediction in large-scale screening and public health research.

• Korean Journal of Cognitive Science
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• v.15 no.1
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• pp.1-11
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• 2004

The aim of this study is to reveal the order and the age of mastery of phonemic contrast in vowel sounds of Korean. For this purpose, we made an observation of the correspondences between the sounds produced by children of 12-35 months and the target sounds produced by adults. The provisional order and the age of contrast acquisition shown from the results of this study are as follows. First, the differential production of vowels by the feature relating to the body of the tongue precedes the differential production of vowels by the feature relating to the lip rounding. Second, as for the differential production of vowels by the feature relating to the body of the tongue, the contrast between the low vowels and the others is accomplished first, and the contrast between the high and low vowels and the contrast between the front and the back vowels are established around the age of 24 months. Third, as for the differential production of vowels by the feature relating to the lip rounding, the contrast between the rounded and the unrounded vowel is not accomplished until 36 months. Finally, we observed, prior to the completion of the differential production of phonemes, children use a specific phoneme excessively. This passing phrase could be interpreted as a result of over-application of a distinctive feature in the course of acquisition of it.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.12
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• pp.133-142
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• 1999

We introduce the algorithms of 2-D and 3-D position estimation using 2-D vision sensors. The sensors used in this research issue red laser slit light to the body. So, it is very convenient to obtain the coordinates of corner point or edge in sensor coordinate. Since the measured points are normally not fixed in the body coordinate, the additional conditions, that corner lines or edges are straight and fixed in the body coordinate, are used to find out the position and orientation of the body. In the case of 2-D motional body, we can find the solution analytically. But in the case of 3-D motional body, linearization technique and least mean squares method are used because of hard nonlinearity.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.226-228
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• 2021

We propose a fall recognition system using the Pose Detection of Google ML kit using video data. Using the Pose detection algorithm, 33 three-dimensional feature points extracted from the body are used to recognize the fall. The algorithm that recognizes the fall by analyzing the extracted feature points uses k-NN. While passing through the normalization process in order not to be influenced in the size of the human body within the size of image and image, analyzing the relative movement of the feature points and the fall recognizes, thirteen of the thriteen test videos recognized the fall, showing an 100% success rate.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.3
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• pp.235-241
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• 2007

This paper proposes a self-calibration method of robots those are used in industrial assembly lines. The proposed method is a position compensation using laser sensor and vision camera. Because the laser sensor is cross type laser sensor which can scan a horizontal and vertical line, it is efficient way to detect a feature of vehicle and winding shape of vehicle's body. For position compensation of 3-Dimensional axis, we applied block interpolation method. For selecting feature point, pattern matching method is used and 3-D position is selected by Euclidean distance mapping between 462 feature values and evaluated feature point. In order to evaluate the proposed algorithm, experiments are performed in real industrial vehicle assembly line. In results, robot's working point can be displayed 3-D points. These points are used to diagnosis error of position and reselecting working point.