Analysis of Motor Control and Learning in Human-Robot Interaction during Game Guided Movements

Robotic devices can be used in upper limb rehabilitation in order to help the total or partial functional recovery. Robots can perform repetitive activities for a long period of time, which may be beneficial for rehabilitation processes. In this context, this study uses a bi-manual robotic device to investigate motor learning and control for the upper limbs among different game guided tasks, and inspect the user’s grip force exerted in response to perturbations. The robotic device resembles a bicycle handlebar, instrumented with load cells to measure torques and grip forces. It is equipped with a DC motor to apply external torques to the guiding system. A game was developed containing in-game and physical perturbations to the natural movement of the handlebar. Tests were carried out with 16 healthy subjects that were instructed to move the handlebar guiding a character displayed on the screen with the objective of collecting tokens to get the higher score in the game. During the trials, corresponding data from the game and the load cells were collected and used to infer the learning process, the mean error in the trajectory and the variations in the force applied to the handles of the handlebar. Analyses showed that there was learning in the first repetitions, and the learning was retained further. The higher values of the grip force occurred when there was a physical perturbation to the handlebar’s natural movement. The larger errors in the trajectories occurred immediately after the perturbations ended. In conclusion, there was a performance improvement, probably related to learning. The increase of the mean error at the transitions of the perturbations indicates the need for adaptation to the new conditions of the task.