• 한국지역사회생활과학회지
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• 제27권3호
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• pp.495-508
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• 2016

This study was conducted to recognize the need for diet and nutrition education to correct body-shape and eating habits that lead to eating disorders in college students. The relationship between diet and obesity was confirmed. Approximately 405 (male 46.4%, female 53.6%) students were evaluated by questionnaire in September 2014. The statistical program SAS (ver. 4.3) was used to evaluate the Chi-squared, F and T-value. The correlation between eating disorder risk and eating habits was evaluated by Pearson's correlation. Body type recognition was classified into nine steps up the body fatty (9) to skinny (1) to show their body. Eating disorder risk (KEAT-26) was composed of F1 (attachment factors for weight loss), F2 (attachment factors for binge eating, and food), and F3 (adjustment factor to eating their will. The risk of eating disorders in male 73.4% of low risk, in female 61.3% (p<0.05). According to body mass index, underweight groups recognized in the normal weight (53.7%), normal weight group was in overweight (29.1%) (p<0.001). According to body-type, the overweight group had a higher risk of eating disorders (68.2%). The KEAT-26 showed that the overweight and obese group were high-risk in F1 & F2, while the underweight group was high-risk in F3 (p<0.001). Recognized overweight showed the dangers of eating disorders, proper recognition of body-type and body mass index required. Tendency to seek a balanced diet was associated with eating disorders, no-imposed adequate diet for nutritional education would be made. Proper nutrition education for males is needed depending on the increased incidence of male eating disorders.

• 전자통신동향분석
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• 제33권3호
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• pp.89-99
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• 2018

Human activity recognition (HAR) is a technology that aims to offer an automatic recognition of what a person is doing with respect to their body motion and gestures. HAR is essential in many applications such as human-computer interaction, health care, rehabilitation engineering, video surveillance, and artificial intelligence. Smartphones are becoming the most popular platform for activity recognition owing to their convenience, portability, and ease of use. The noticeable change in smartphone-based activity recognition is the adoption of a deep learning algorithm leading to successful learning outcomes. In this article, we analyze the technology trend of activity recognition using smartphone sensors, challenging issues for future development, and a strategy change in terms of the generation of a activity recognition dataset.

• 전기학회논문지P
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• 제65권2호
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• pp.130-135
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• 2016

Gait recognition has advantage of non-contact type recognition. But It has disadvantage of low recognition rate when the pedestrian silhouette is changed due to bag or coat. In this paper, we proposed new method using combination of gait energy image feature and thermal face image feature. First, we extracted a face image which has optimal focusing value using human body rate and Tenengrad algorithm. Second step, we extracted features from gait energy image and thermal face image using linear discriminant analysis. Third, calculate euclidean distance between train data and test data, and optimize weights using genetic algorithm. Finally, we compute classification using nearest neighbor classification algorithm. So the proposed method shows a better result than the conventional method.

• 한국의류산업학회지
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• 제14권5호
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• pp.811-820
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• 2012

This study identifies the factor structure of sociocultural attitudes toward appearance, appearance management attitudes, fashion behaviors, and the effects of sociocultural attitudes toward appearance and appearance management attitudes on fashion behaviors. Questionnaires were administered to 255 college students living in Daegu Metropolitan City and Kyungbook province. The data collected were analyzed using frequency, factor analysis, multiple regression, and t-test. The findings were as follows. Sociocultural attitudes toward appearance were composed of two factors (body internalization and appearance recognition). Appearance management attitudes were composed of four factors (shape management recognition, weight management, appearance satisfaction, and health care). Fashion behaviors were composed of six factors (convenience, fashion following, self-satisfaction, attractiveness, pursuit of change, and pleasure). Gender of college students showed a distinction between the sub-variables of sociocultural attitudes toward appearance factors (body internalization) and appearance management attitudes (weight management) and fashion behaviors (convenience, fashion following, self-satisfaction, and pleasure). Women had more desire to have a similar looking body of TV celebrities than men. The effects of sociocultural attitudes toward appearance and appearance management attitudes on each fashion behavior variables (convenience, fashion following, self-satisfaction, attractiveness, pursuit of change, and pleasure) were explained by the factors of body internalization and appearance recognition, and weight management, appearance satisfaction, and health care. College students produced fashion appropriate to the situation. Strategies of fashion marketing based on these results are as follow. Through mass media, advertisers help university students realize a healthy outlook and create a social atmosphere that can promote healthy body attractions.

• 대한전기학회논문지:시스템및제어부문D
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• 제55권2호
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• pp.82-89
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• 2006

The electroencephalogram generated by light stimulation in human body of dark adaption state was measured and analyzed in the cases that electromagnetic wave was put in and not put in, respectively. Shieldroom being able to interrupt the light and the electromagnetic wave of outer was constructed, and the experimental system being able to apply any light and any electromagnetic wave was designed. When the electromagnetic wave was applied to body or not, the variation characteristics of each component in the electroencephalogram were i3s follows. The 6 wave was increased and the $\alpha$ wave and the $\beta$ wave were decreased in the case that the electromagnetic wave was applied, and the variation range of the $\Theta$ wave was small. And the influence of electromagnetic wave on human body was that the appearance time of the $\beta$ wave was late, and it moaned that the time of visual recognition was delayed.

• 제어로봇시스템학회논문지
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• 제14권2호
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• pp.178-183
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• 2008

Human motion analysis is researched as a new method for human-robot interaction (HRI) because it concerns with the key techniques of HRI such as motion tracking and pose recognition. To analysis human motion, extracting features of human body from sequential images plays an important role. After finding the silhouette of human body from the sequential images obtained by CCD color camera, the skeleton model is frequently used in order to represent the human motion. In this paper, using the silhouette of human body, we propose the feature extraction method based on hybrid skeleton for detecting human motion. Finally, we show the effectiveness and feasibility of the proposed method through some experiments.

• 한국멀티미디어학회논문지
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• 제8권3호
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• pp.322-327
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• 2005

본 연구에서는 인체에 대한 3차원 입체 정보를 포함하는 레이저 스트라이프 신호를 분석하여 인체를 인식 할 수 있는 한 방법을 제안하였다. 레이저 스트라이프 신호를 투시한 영상은 3차원 정보를 가지고 있으며 특히 사람에게 투사된 레이저 스트라이프 신호를 분석하여 사람의 특성을 나타내는 패턴 파형이 있음을 확인하였다. 그리하여 본 연구에서는 이 특징 파형을 푸리에 서술자를 통하여 저장한 후 사람에게 스캔한 스트라이프 신호를 입력 비교하였으며 특정 자세에 대해서 인체를 인식할 수 있음을 확인할 수 있었다. 앞으로 더욱 다양한 자세에 대한 특징 패턴을 분석하여 복잡한 인체의 모습을 인식할 수 있으리라 기대한다.

• 패션비즈니스
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• 제19권4호
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• pp.168-182
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• 2015

The purpose of this study is to classify students' attitudes towards uniform modification and analyze their subjective experience regarding appearance, and body satisfaction. Questionnaires were administered to 369 middle and high school girls living in Deagu Metropolitan City. Frequency, factor analysis, reliability analysis, correlation analysis, ${\chi}^2$-test, and t-test are all used for data analysis. Our findings are as follows. Two hundred thirty students (62.3%) agreed to modify their school uniforms to express their personalities and follow fashion trends. Motives for uniform modification had to do with social life, physical attractiveness, and practicality. Attitudes toward appearance are found to be shaped by appearance internalization, active management of appearance, appearance needs, social recognition, and conformity. The motives for uniform modification reveal a significant correlation with attitudes toward appearance. Uniform modification satisfaction differed depending on sub-variables of attitude toward appearance(active management of appearance, personalized appearance needs, social recognition, and body satisfaction, such as satisfaction with height and BMI). There was a significant difference in expression of intention for future plastic surgery depending on body image.

• 사상체질의학회지
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• 제16권2호
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• pp.32-43
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• 2004

1. Objectives This paper was tried to find out Dong-Mu's recognition systemfor Sasang Structure about "Dongyi Suse Bowon" and "Gyukchigo" 2. Methods The study was researched through comparative studying in a structural manner between "Gyukchigo" 's Affairs-Mind-Body-Objects(事心身物) and "Dongyi Suse Bowon" 's Heaven-Human-Nature-Order(天人性命). 3. Results and Conculusions "Gyukchigo" 's Affairs-Mind-Body-Objects(事心身物) should be understood as Dong-Mu's change process of thinking scheme before writing the book of "Dongyi Suse Bowon" and it is somewhat difficult to make a connected attachment in a lump between "Dongyi Suse Bowon" 's Heaven-Human-Nature-Order(天人性命) and "Gyukchigo" 's Affairs-Mind-Body-Objects(事心身物). Just "Dongyi Suse Bowon" 's Heaven-Human-Nature-Order(天人性命) can be understood as a new recognition systemto encompass" Gyukchigo" 's Affairs-Mind-Body-Objects(事心身物).

• 한국정보통신학회논문지
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• 제21권12호
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• pp.2365-2370
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• 2017

본 논문에서는 체험형 VR 환경을 고려한 동작 및 위치 인식 기법을 제안한다. 동작 인식은 신체부위에 복수개의 AHRS 디바이스를 부착하고 이를 기준으로 좌표계를 정의한다. 각각의 AHRS 디바이스로부터 측정되는 9축 움직임 정보를 기반으로 사용자의 동작을 인식하고 신체 분절 간의 관절각을 추출하여 동작을 보정한다. 위치인식은 AHRS 디바이스의 관성센서를 통해 보행 정보를 추출하여 상대위치를 인식하고 BLE Fingerprint를 이용하여 누적오차를 보정한다. 제안하는 동작 및 위치인식 기법의 구현을 위해 AHRS기반의 위치인식과 관절각 추출 실험을 진행하였다. 위치 인식 실험의 평균 오차는 0.25m, 관절 각 추출 실험에서 관절 각 평균 오차는 $3.2^{\circ}$로 나타났다.