A Novel Assist-as-Needed Controller for Locomotion Training in the fMRI

Abstract

In this thesis it is explained the work done in order to develop a patient-cooperative control that assists only as it is needed for the MR-compatible stepper (MARCOS) developed at the Sensory Motor System Lab of ETH Zürich. The so called assist-as-needed (AAN) control algorithm adapts the robotic assistance to optimize the patient’s effort and the error generated while performing a task. This algorithm has already been applied to some rehabilitation devices. In this work an implementation of such strategy for MARCOS is described. It is shown that in MARCOS an AAN has to be based on a force controller due to some limitations related to its pneumatic actuation. Preliminary results of the new controller’s performance are provided. A first experiment based on EMG-measured muscular activity reported a correlation between assistance level and Bicep Femoris activity, evolving from a patient-passive to a patient-active situation. A second experiment aimed to observe the brain activity related to the assistance. It also showed a negative dependence between primary motor cortex activity and the degree of assistance. Furthermore, an active-like activation of the brain was observed during the AAN. The results obtained showed the adequacy of the new controller to help the patient and, at the same time, stimulate him/her to participate in the walking task. Finally, the controller is compared to those found in literature. It is discussed the advances that such AAN controller may bring for future research projects, as its force controller-based implementation brings a proper scenario to a natural evolution from assistive rehabilitation strategies to resistive ones.