Improved mobility and faster recovery for stroke patients

Tuesday, 12 May, 2015
A person who has suffered a stroke very often faces a long rehabilitation. In H2020, the RETRAINER project will use robot-based technologies to speed up the rehabilitation and allow the users to use their own arm and hand as much and as soon as possible after the trauma so to achieve the best outcomes in the rehabilitation.

Strokes cost for the healthcare alone is estimated at 27 billion euro per year within the EU. Added to this is the loss of income as well as the difficulties facing stroke patients and families in daily activities that a healthy person takes for granted, such as eating or getting dressed. RETRAINER will be working on two systems that could be either used in combination or as a stand-alone to help people with upper-limb disabilities. One is a robot, which is not meant to completely take over the user’s tasks or substitute the functionality of the body, but will support the user only whenever he/she really needs support. If the person has some functionality left in the arm the robot helps train and improve the mobility of the limb rather than replace all the natural functions. A passive exoskeleton provides weight relief and Neuromuscular Electrical stimulation (NMES) delivered to several arm muscles stimulating them to encourage natural arm movements.

The second part of RETRAINER is a wearable NMES system with multiple arrays of electrodes for hand rehabilitation facilitating the grasping function.

RETRAINER is an H2020 project partly funded by the EU. The consortium is led by ABACUS and contains 9 partners from Austria, Germany, Italy and Switzerland. It is due to receive 2.8 million euro over 4 years and just started working in January this year. It has provided a video presenting the project, which you can view here. The project is using and building on the results of a previous FP7 project, MUNDUS, aimed at setting up a similar assistive device for severely disabled people in daily life activities.

REaching and grasping Training based on Robotic hybrid AssIstance for Neurological
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