3차원 데이터를 활용한 학령기 남아의 상반신 체형 분류
Upper Body Type Classification of Elementary School Boys Using 3D Data
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초록
This study classified and analyzed the upper body types of 7-13 years old elementary school boys, using 3D data from the 6th Size Korea. The results of this study are as follows. Seven factors were extracted from the factorial analysis as an independent factor for a cluster analysis. The cluster analysis generated four body types. Type 1 has large ratio of front and back depth as well as circumference, with a front protrusion. In Type 2, the vertical value of upper torso is longer than average; in addition, its flatness is the largest and produces a thin body type. Type 3 has a smaller flatness in the bust, waist, abdomen and hip than other types, while also having the largest BMI. Type 4 is characterized by a greater shoulder angle than other types and its other factors are close to average. As a result of the logistic regression analysis, the prediction model used eight variables to generate and its accuracy is 88.679%. The classification of upper body types from this study can be used as basic data to improve patternmaking for each body type. The generated prediction model is also expected to be used as a method to help classify upper body types using the eight variables.
Abstract
This study classified and analyzed the upper body types of 7-13 years old elementary school boys, using 3D data from the 6th Size Korea. The results of this study are as follows. Seven factors were extracted from the factorial analysis as an independent factor for a cluster analysis. The cluster analysis generated four body types. Type 1 has large ratio of front and back depth as well as circumference, with a front protrusion. In Type 2, the vertical value of upper torso is longer than average; in addition, its flatness is the largest and produces a thin body type. Type 3 has a smaller flatness in the bust, waist, abdomen and hip than other types, while also having the largest BMI. Type 4 is characterized by a greater shoulder angle than other types and its other factors are close to average. As a result of the logistic regression analysis, the prediction model used eight variables to generate and its accuracy is 88.679%. The classification of upper body types from this study can be used as basic data to improve patternmaking for each body type. The generated prediction model is also expected to be used as a method to help classify upper body types using the eight variables.
참고문헌 (25)
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