The difference in joint torque of lower extremity during vertical jump by using different body segment parameter in gymnast

• Main Authors: Ching-Wen Hu, 胡瀞文
• Other Authors: Tzyy-Yuang Shiang
• Format: Others
• Language: zh-TW
• Published: 2004
• Online Access: http://ndltd.ncl.edu.tw/handle/06937451437378119399

碩士 === 國立體育學院 === 運動科學研究所 === 92 === Body segment parameters(BSPs) is an important data of biomechanics research, due to the difference of races, age, sex, fitness level, sports event and other factors of subjects, which would lead to disparity between reality and research results. The purposes of this study is using five different BSPs models to calculate the horizontal ground reaction force, vertical ground reaction force and net muscle torque of gymnasts during vertical jump, to compare and discuss the difference caused by using different body segment parameters. There were four Taiwanese elite gymnasts participated in this study, depend on inverse dynamics theory, by using three dimension force plate and high speed digital camera to obtain the ground reaction force and kinematics data during vertical jump, then applied five BSP models to calculate the horizontal ground reaction force, vertical ground reaction force and net muscle torque of each joint of lower extremity. The five BSPs models included cadaver method （Dempster,1955）,gamma-ray method (Zasiorsky,1983), and young Taiwanese male, athletes and subjects’ individual BSPs which established by magnetic resonance imaging(MRI) method（Ho,2002）. subjects’ individual BSPs for standard to calculate SEE(standard estimate error) value and compare the difference of kinetic data between the BSPs models. This study results indicated that the SEE value of gamma-ray method was the smallest, and the cadaver method was obviously greater than other BSPs models. According to the results of this study, recommend that while doing study of Taiwanese gymnast, if the subjects’ individual BSPs is not available, use BSPs established by gamma-ray method would reduce the error. When choosing other BSP models, should consider the similarity between sample of choosing method and subject of research, such as sex, age, race, living style, diet, fitness level, and representative of samples, choosing appropriate BSPs models can reduce the inaccuracy and improve the practicability of results.