A bio-inspired controller of an upper arm model in a perturbed environment

In humans, multijoint tasks are executed through the integration of sensory information, sensorimotor transformations and motor planning. Computational models can be profitably used to gain knowledge on the mechanisms sub-serving these three aspects of motor control. In this general context, artificial neural networks represent a means to represent and interpret the movement of upper limb in normal and altered conditions. In the present work a controller of an upper human arm model based on an artificial neural network is being exposed to different conditions simulate altered force environment, to give insights on the adaptation ability of the human arm to environmental modifications such as the insertion of different force fields acting on the end-effector.