Project Summary: Current robotic neurorehabilitation strategies for motor skills in stroke patients showed large variability in their outcome. Some patients show very promising improvement in their motor skills whereas others show no improvement at all. The main issue is that the outcome of the robotic rehabilitation therapy cannot be measured in advance for now. We try to overcome this problem by predicting the outcome of a patient prior to exposing him to a robotic rehabilitation therapy.

By trying to understand the motor learning and motor adaptation processes in the lower limb, we hope to be able to assess patient outcomes prior to the robotic therapy. For this assessment, we use the Lokomat robotic platform (Hocoma, Switzerland) and a variant of the Force-Field Adaptation Paradigm for the lower extremities. We also develop algorithms to assess single patient motor learning and adaptation capabilities. This should provide us insight on which aspect of motor learning and motor adaptation is important for improvement in gait retraining.