Identification of kinematic traits in 3d reaching tasks with the use of a haptic nine-hole peg-board test: Comparison between healthy people and people with Multiple Sclerosis

Abstract

Multiple sclerosis (MS) is a complex neurological disease that destroys the myelin sheath of the nerves, consequently affecting the motor control ability of the person in numerous ways. Rehabilitation of the upper limb with the help of robots has been proved beneficial for people with MS. Nevertheless, most of the state-of-the-art robotic rehabilitation systems limit the training exercises to reaching tasks that are constrained on planar motion. In order to provide complete and task-specific therapy, it could be more beneficial to take advantage of the robotspsila ability to move and provide force feedback in generic three dimensional motions that are more realistic. In this work, a force control approach for therapy in complex three dimensional reaching tasks is proposed. The control concept is developed by identifying, analyzing and comparing kinematic traits of healthy people and people with MS in reaching tasks. More specifically the measurements take place within a previously developed haptic-interface-based Nine-Hole Peg-Board Test (NHPT) that facilitates the generation of 3D reaching tasks of various directions, distances and time-durations. The results suggest that both groups have similar spatial motion characteristics; movement is bounded on planes with similar distribution of orientations in space, and with similar plane-projected trajectory shapes. Nevertheless, differences are found in the duration of the movements; significantly longer durations are needed in the case of people with MS, and as seen by comparing the time-distance curves of the two groups, this difference in duration increases with target distance. Based on these findings, a robotic assessment procedure for complex reaching tasks can be developed. Also with the use of the findings and by employing a 3D Minimum Jerk Model (MJM), constrained by initial and final velocities and positions, a force-control law for upper limb robotic training of people with MS in 3D reaching tasks can now be designed.