ARTORG Center for Biomedical Engineering Research

Relearning to walk with exoskeletons – effect of wearable robots on human motivation

Project Members:  Laura Marchal-Crespo

Robot-aided gait rehabilitation is a promising technique to improve rehabilitation in patients with neurological injuries. During robotic gait-training, patients are assisted with physical guidance from a robotic device to move their legs into a correct gait pattern. Although robotic haptic guidance is the commonly used motor-training strategy to reduce performance errors while training, research on motor learning has emphasized that errors are in fact a fundamental neural signal that drives motor adaptation. Thus, researchers have proposed robotic therapy algorithms that amplify movement errors rather than decrease them.

The goal of this project is to evaluate the effect of haptic error modulating controllers on relearning to walk. We are especially interested in evaluating the effect of modulating the errors on subjects’ motivation. Feedback has motivational properties that have an important influence on learning. For example, feedback after successful trials has been shown to have a beneficial effect on learning.

The experiments are carried out using MARCOS (MAgnetic Resonance COmpatible Stepper) developed at ETH Zurich as a tool for the investigation of the neural correlates underlying the control of lower limb movements (Marchal-Crespo et al. 2017). The knee and the foot of the subjects are each connected to a pneumatic actuator.The position of the leg and external forces (e.g. to simulate ground reaction forces) are controlled by theses cylinders separately for each leg. Movements performed with MARCOS are comparable to periodic, on the spot stepping.

Keywords: Wearable robotics, exoskeleton, rehabilitation, neural correlates, motor learning, motivation, error modulating controllers

Marchal-Crespo L, Michels L, Jaeger L, López-Olóriz J, Riener R (2017). Effect of Error Augmentation on Brain Activation and Motor Learning of a Complex Locomotor TaskFrontiers Neuroscience, 11:526. doi: 10.3389/fnins.2017.00526