AUTHOR=Grimm Florian , Naros Georgios , Gharabaghi Alireza 

TITLE=Closed-Loop Task Difficulty Adaptation during Virtual Reality Reach-to-Grasp Training Assisted with an Exoskeleton for Stroke Rehabilitation

JOURNAL=Frontiers in Neuroscience

VOLUME=10

YEAR=2016

URL=https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2016.00518

DOI=10.3389/fnins.2016.00518

ISSN=1662-453X

ABSTRACT=<p>Stroke patients with severe motor deficits of the upper extremity may practice rehabilitation exercises with the assistance of a multi-joint exoskeleton. Although this technology enables intensive task-oriented training, it may also lead to slacking when the assistance is too supportive. Preserving the engagement of the patients while providing “assistance-as-needed” during the exercises, therefore remains an ongoing challenge. We applied a commercially available seven degree-of-freedom arm exoskeleton to provide passive gravity compensation during task-oriented training in a virtual environment. During this 4-week pilot study, five severely affected chronic stroke patients performed reach-to-grasp exercises resembling activities of daily living. The subjects received virtual reality feedback from their three-dimensional movements. The level of difficulty for the exercise was adjusted by a performance-dependent real-time adaptation algorithm. The goal of this algorithm was the automated improvement of the range of motion. In the course of 20 training and feedback sessions, this unsupervised adaptive training concept led to a progressive increase of the virtual training space (<italic>p</italic> &lt; 0.001) in accordance with the subjects' abilities. This learning curve was paralleled by a concurrent improvement of real world kinematic parameters, i.e., range of motion (<italic>p</italic> = 0.008), accuracy of movement (<italic>p</italic> = 0.01), and movement velocity (<italic>p</italic> &lt; 0.001). Notably, these kinematic gains were paralleled by motor improvements such as increased elbow movement (<italic>p</italic> = 0.001), grip force (<italic>p</italic> &lt; 0.001), and upper extremity Fugl-Meyer-Assessment score from 14.3 ± 5 to 16.9 ± 6.1 (<italic>p</italic> = 0.026). Combining gravity-compensating assistance with adaptive closed-loop feedback in virtual reality provides customized rehabilitation environments for severely affected stroke patients. This approach may facilitate motor learning by progressively challenging the subject in accordance with the individual capacity for functional restoration. It might be necessary to apply concurrent restorative interventions to translate these improvements into relevant functional gains of severely motor impaired patients in activities of daily living.</p>