Estimating the optimum exercise pattern in upper extremity using musculoskeletal modeling and genetic algorithm

Abstract Today, numerous training techniques, involving various motion patterns, are used for developing musculoskeletal system abilities in sport activities and rehabilitation programs. The controversial question is that, which of these exercises, have more performance ability to lead to best results in each case. In this paper an optimized tool has been presented to compare different motion patterns in human arm. This tool, not only made it possible to compare different motion pattern but also it can be utilized for suggesting theoretical optimum exercises. These exercises can be generated base on different purposes such as, create more challenge in specific muscle group or decrease injury risk at special joins. The scripts have been developed to interact with the OpenSim software API from Matlab working environment. This is an optimization process based on genetic algorithm to obtain optimum pattern of exercise. The estimated results for muscle activities have been validated by measuring EMG signals during several different trajectories. Two degrees of freedom exoskeleton with haptic controller has been employed for tracing different trajectories and measuring kinematic and kinetic parameters. The results suggest that, small change in specific exercise may be leads to great change in activation pattern of musculotendon actuators. So, it's extremely important to select the good exercise and correct implementation of it. The good correlation between numerical and experimental results confirm that presented numerical procedure provides good way of predicting and understanding the effects of different exercises technique in rehabilitation process or sport activities.