Towards more efficient robotic gait training: A novel controller to modulate movement errors

Abstract

Robot-aided gait training has been presented as a promising technique to improve rehabilitation in patients with neurological lesions. Although robotic guidance is often used to reduce performance errors while practicing, there is currently little evidence that robotic guidance is more beneficial for human motor learning than unassisted practice. Research on motor learning has emphasized that movement errors drive motor adaptation. Thereby, robotic algorithms that augment errors rather than decrease them have a great potential to provoke better motor learning. In this paper, we present a novel control algorithm that modulates movement errors by limiting dangerous and discouraging large errors with haptic guidance, while augmenting awareness of task relevant errors by means of error amplification. We also designed a controller that applies random disturbance torques that can work on top of the error-modulating controller. The controllers were evaluated using robotic testing. The error-modulating controller resulted in larger errors due to error amplification, but limited the maximum deviation from the desired pattern, thanks to haptic guidance. Adding random disturbance torques increased gait variability. The combination of the random disturbance and error-modulating controllers increased the kinematic errors and gait variability, while limited large errors, providing an excellent framework to enhance motor learning.