Prediction of Body Center of Mass Acceleration From Trunk and Lower Limb Joints Accelerations During Quiet Standing
Journal Title: Physical Treatments: Specific Physical Therapy Journal - Year 2017, Vol 7, Issue 2
Abstract
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.
Authors and Affiliations
Leila Ghazaleh, Mehrdad Anbarian, Mohsen Damavandi
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