شنبه 3 آذر 1403
دوره 7، شماره 2 - ( 4-1396 )
جلد 7 شماره 2 صفحات 112-103 |
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Ghazaleh L, Anbarian M, Damavandi M. Prediction of Body Center of Mass Acceleration From Trunk and Lower Limb Joints Accelerations During Quiet Standing . PTJ 2017; 7 (2) :103-112
URL: http://ptj.uswr.ac.ir/article-1-328-fa.html
URL: http://ptj.uswr.ac.ir/article-1-328-fa.html
غزاله لیلا، عنبریان مهرداد، دماوندی محسن. Prediction of Body Center of Mass Acceleration From Trunk and Lower Limb Joints Accelerations During Quiet Standing . فصلنامه فیزیک درمانی. 1396; 7 (2) :103-112
لیلا غزاله* 
1، مهرداد عنبریان2 ، محسن دماوندی3
1، مهرداد عنبریان2 ، محسن دماوندی3
1- ایران، تهران، دانشگاه الزهرا، دانشکده علوم ورزشی
2- ایران، همدان، دانشگاه بوعلی سینا، دانشکده علوم ورزشی، گروه بیومکانیک ورزشی
3- ایران، سبزوار، دانشگاه حکیم سبزواری، دانشکده علوم ورزشی
2- ایران، همدان، دانشگاه بوعلی سینا، دانشکده علوم ورزشی، گروه بیومکانیک ورزشی
3- ایران، سبزوار، دانشگاه حکیم سبزواری، دانشکده علوم ورزشی
چکیده: (4574 مشاهده)
Purpose: Predicting body Center of Mass (COM) acceleration is carried out with more accuracy based on the acceleration of three joints of lower limb compared to only accounting joints of hip and ankle. Given that trunk movement during quite standing is noticeable, calculating trunk acceleration in model might increase prediction accuracy of COM acceleration. Moreover, in previous research studies, dominant and nondominant limb was neglected as influencing variables in prediction accuracy. Therefore, this study aimed to investigate the accuracy of predicting COM acceleration based on the accelerations of lower limb joints and trunk, with emphasis on weight distribution on legs.
Methods: The relevant kinematic data were collected using motion analysis systems. In this regard, visual 3D software was used to create a 14-segment model for each subject and estimate the positions of body COM. A force plate was used to assess the body weight distribution between legs. Calculation of the summation of joints angular accelerations was done using algebraic addition of time series data.
Results: Prediction of COM acceleration, based on the individual acceleration of lower limb joints and trunk, revealed that just acceleration of hip joint in both dominant and nondominant lower limbs was an appropriate variable for predicting COM acceleration (R2adj =0.40). However, during prediction based on the summation of trunk and joints accelerations, its accuracy showed a significant increase (R2adj=0.90).
Conclusion: The summation of angular accelerations of trunk and lower limb joints is the most accurate predictor of COM acceleration during quiet standing balance control. Simultaneous changes of lower limb joints and trunk accelerations control COM acceleration.
Methods: The relevant kinematic data were collected using motion analysis systems. In this regard, visual 3D software was used to create a 14-segment model for each subject and estimate the positions of body COM. A force plate was used to assess the body weight distribution between legs. Calculation of the summation of joints angular accelerations was done using algebraic addition of time series data.
Results: Prediction of COM acceleration, based on the individual acceleration of lower limb joints and trunk, revealed that just acceleration of hip joint in both dominant and nondominant lower limbs was an appropriate variable for predicting COM acceleration (R2adj =0.40). However, during prediction based on the summation of trunk and joints accelerations, its accuracy showed a significant increase (R2adj=0.90).
Conclusion: The summation of angular accelerations of trunk and lower limb joints is the most accurate predictor of COM acceleration during quiet standing balance control. Simultaneous changes of lower limb joints and trunk accelerations control COM acceleration.
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