RehabNet builds on several principles to develop the next generation of motor rehabilitation systems after stroke.
Since 85% of stroke survivors will present a motor deficit, it is important to design a system that can be used by the widest range of patients, and in particular by those with worse prognostic. Our partnership with Myomo Inc. (Boston, USA) in this project allows us to take advantage of a unique wearable and portable robotic device that restores correct limb position with integrated EMG measurement capabilities (mpower1000).
Our neurorehabilitation training paradigm takes into account concepts of occupational and physical therapy, motivational and engagement factors intrinsic to gaming, and robotics, and puts them at the service of a clear neuroscientific hypothesis on how to effectively mobilize brain plasticity for a functional recovery. In RehabNet we will develop in close collaboration with our clinical partner, the Hospital of Funchal (SESARAM), a combination of a VR training task with an automatically adjustable robotic assistance level.
3) Multimodal data
In order to understand the plastic changes that the brain undergoes during the upper-limb rehabilitation process, we need to be able to synchronously collect data on the patient behavior (his/her physical movements and their quality), analyze how behavior relates to task performance (successful vs. failed motor actions), and assess which are the particular brain activity patterns that relate behavior
with performance and successful functional recovery (EEG).