Society’s emphasis on a thin body ideal may intensify an individual’s negative perceptions of fatness. The purpose of the present study was to examine the relationship between sociocultural attitudes toward appearance (awareness and internalization of the female ideal) and anti-fat attitudes among middle-aged Korean women. In addition, the aim was to examine whether the body internalization of female ideals was a mediator in the proposed model. Participants included a sample of 264 middle-aged Korean women who completed a series of measures online. The following information was collected through online questionnaires: awareness and internalization of the female ideal, attitudes toward fat, body weight perception, and demographics. Data were analyzed using correlation analysis, descriptive analysis, factor analysis, and structural equation modeling. The measurement model and the structural model testing provided an acceptable fit to the data, and all proposed pathways in the research model were statistically significant. Awareness of the female ideal was significantly and positively associated with internalization, and it significantly and positively predicted both constructs of anti-fat attitudes. Additionally, awareness of the female ideal was significantly and positively indirectly associated with attitudes toward fat peopledislike and willpower mediated by internalization. Overall, these findings suggest that society’s emphasis on female appearance and a thin body can ultimately result in significant stigmatization of overweight/obese individuals. This study emphasizes the importance of establishing a healthy appearance standard to reduce anti-fat prejudice.

최근 스크린 클라이밍용 콘텐츠로 클라이밍 학습 프로그램과 스크린 클라이밍 게임이 등장하였으 며, 특히 스크린 클라이밍 게임에 대한 연구가 활발히 진행되고 있다. 본 논문에서는 스크린 클라이 밍 콘텐츠 구현의 핵심 기술인 자세 인식 성능의 개선을 위하여 등반자의 신체영역을 기반으로 하 는 스켈레톤 보정 방법을 제안한다. 스켈레톤 보정 과정은 비정상적인 스켈레톤 정보를 걸러내는 스켈레톤 프레임 안정화와 신체 영역을 관절부위별로 나누어 각 관절부위의 중점을 보정위치로 하 는 신체영역 기반 스켈레톤 수정 과정으로 이루어진다. 이렇게 보정한 스켈레톤 정보는 클라이밍 콘텐츠에서 등반자의 자세가 이상적인 자세와 얼마나 유사한지 판단하는 데 사용될 수 있다.

Body gesture Recognition has been one of the interested research field for Human-Robot Interaction(HRI). Most of the conventional body gesture recognition algorithms used Hidden Markov Model(HMM) for modeling gestures which have spatio-temporal variabilities. However, HMM-based algorithms have difficulties excluding meaningless gestures. Besides, it is necessary for conventional body gesture recognition algorithms to perform gesture segmentation first, then sends the extracted gesture to the HMM for gesture recognition. This separated system causes time delay between two continuing gestures to be recognized, and it makes the system inappropriate for continuous gesture recognition. To overcome these two limitations, this paper suggests primitive body model encoding, which performs spatio/temporal quantization of motions from human body model and encodes them into predefined primitive codes for each link of a body model, and Selective/Asynchronous Input-Parallel State machine(SAI-PSM) for multiple-simultaneous gesture recognition. The experimental results showed that the proposed gesture recognition system using primitive body model encoding and SAI-PSM can exclude meaningless gestures well from the continuous body model data, while performing multiple-simultaneous gesture recognition without losing recognition rates compared to the previous HMM-based work.