Optimal torque adaptation in bimanual assisted rehabilitation

Automation of neurologic rehabilitation becomes more important as the number of age correlated dysfunctions increases. Starting from existing Assist-As-Needed control schemes we designed new algorithms specifically for bilateral tasks. We propose three approaches for controlling an arm rehabilitation device that assists hemiparetic subjects. Design goals are 1) Users should use there healthy arm to generate the desired movement pattern. 2) Force contribution and freedom of movement should be as high as possible for the affected arm. 3) Large errors of the affected arm must be compensated by the device. Simulations and first clinical evaluations support the following results. Goal 1) is achieved by making support independent of time and a set trajectory. Goals 2) and 3) can be achieved with an iterative learning controller that learns a supportive force. Adaptation speed and error sensitivity can be improved with model based approaches. Frequency dependent shifting of the force profile shifts the acceleration to the movement phase in which it is needed resulting in earlier acceleration and deceleration. An additional stiffness should only be used, if the patient's behavior varies to much.