Optimization-based subject-specific planar human vertical jumping prediction: Effect of elbow flexion and weighted vest

Abstract

Jumping strategies differ considerably depending on athletes’ physical activity demands. In general, the jumping motion is desired to have excellent performance and low injury risk. Both of these outcomes can be achieved by modifying athletes’ jumping and landing mechanics. This paper presents a consecutive study on the optimization-based subject-specific planar human vertical jumping to test different loading conditions (weighted vest) during jumping with or without elbow flexion during the arm-swing based on the validated prediction model in the first part of this study. The sagittal plane skeletal model simulates the weighting, unweighting, breaking, propulsion phases and considers four loading conditions: 0%, 5%, 10%, and 15% body weight. Results show that the maximum ground reaction forces, the body center of mass position, and velocities at the take-off instant are different for different loading conditions and with/without elbow flexion. The optimization formulation is solved using MATLAB^® with 35 design variables with 197 nonlinear constraints for a five-segment body model and 42 design variables with 227 nonlinear constraints for a six-segment body model. Both models are computationally efficient, and they can predict ground reaction forces, the body center of mass position, and velocity. This work is novel in the sense that presents a simulation model capable of considering different external loading conditions and the effect of elbow flexion during arm swing.