Optimization-based dynamic prediction of kinematic and kinetic patterns for a human vertical jump from a squatting position

Abstract

This paper presents the prediction of kinematic and kinetic patterns for human squat jumping using an optimization-based dynamic human movement prediction technique. This method enables prediction of realistic kinematics and kinetics in human squat vertical jumping including muscle and joint forces. The case of vertical jumping is selected because the criterion is clear: to maximize the jump height. First, an anatomically detailed three-dimensional human squat jump model was developed. The movement was then parameterized by means of time functions controlling selected degrees-of-freedom (DOF) of the model. Subsequently, the optimizer found the parameters of these functions to maximize the jump height subject to anatomical and physiological constraints. The results were compared with experimental data from a group of six healthy males. Qualitative and quantitative comparisons between predicted results and experimental observations indicate that the approach is capable of predicting the jump height enhancement in squat vertical jumping with arm swing and reproducing the coordinated motion in terms of kinetics and kinematics.

Evidence and mechanism by which upper partial fibulectomy improves knee biomechanics and decreases knee pain of osteoarthritis

“…The kinetic data were down‐sampled to 250 Hz and synchronized in time with the kinematic data. These data were processed in AMS to drive the three‐dimensional gait model (Figure D ) . In addition, the same 20 healthy controls (10 males and 10 females) were also included.…”

Inverse optimal control with time-varying objectives: application to human jumping movement analysis

“…Meghdari and Aryanpour 12 developed a full body, sagittal plane dynamic jumping model framework that could calculate and reproduce the kinematics and dynamics of real jump trajectories recorded through image data. Farahani et al 13 also proposed a full body, sagittal dynamic jumping model that computes the dynamics of vertical jumps from motion capture data.…”

“…One important characteristic of complex movements is the possibility that the control objectives may not be constant throughout the movement. For example, when trying to jump as far as possible, the jumper might first try to optimise the centre of mass (CoM) trajectory at takeoff [11][12][13] , then achieve a preferred posture during the flight phase, and finally minimise impact on landing.…”

Prediction of closed-chain human arm dynamics in a crank-rotation task

“…We select the crank angular motion (ϴ Crank ) to be controlled and parameterize it by using 14 control points over the entire simulation time by means of a fourth-order B-spline curve. For the relatively fast motions, 10-15 control points will suffice for discretization of the time domain (Davoudabadi Farahani et al, 2016). It should be noticed that the number of control points directly influence the computation time.…”

“…In Contents lists available at ScienceDirect journal homepage: www.elsevier.com/locate/jbiomech www.JBiomech.com this paper, movement is treated as independent input and muscle and joint forces are the outputs. This technique was demonstrated already by Rasmussen et al (2000) on a simple, hard-coded pedaling model without experimental validation and was named "inverse-inverse dynamics" because it wraps an outer optimization loop around the inverse dynamics analysis to optimize the control points according to the user-defined objective function (Davoudabadi Farahani et al, 2016). Progress in the field of human posture and movement prediction using an optimization-based inverse dynamics formulation was summarized by Abdel-Malek and Arora (2013) under the name "predictive dynamics".…”