A coordination model based control of functional arm manipulation by RBF neural networks

A model based control system for neuro-rehabilitation of the upper arm in post-stroke hemiplegic patients was developed. The control system was based on normal values of motion parameters of 6 daily task activities. Kinematic data (6 arm joint angles) was measured by using gonio and torsiometers. From computed angular velocities, the following sequences were extracted: reaching & grasping, manipulation, releasing, and returning hand to resting position. The angular accelerations were calculated in order to create synergies in the form of phase plots used to train radial basis function (RBF) neural networks. The networks generated automatic synergy recognition and classification of arm movements in regard to two workspace attributes: distance and laterality of the object position. The synergies have been used in order to shift the control of multijoint arm movements to a higher level and minimize the number of unique couplings between joint accelerations, which define the task, position, or their combination. One task, eating finger food, was selected to illustrate the methodology as an example of precision grasp.