Reaching Training Based on Robotic Hybrid Assistance for Stroke Patients

Stroke is the third most common cause of death and the main cause of acquired adult disability in high-income countries. The most common deficit after stroke is motor impairment of the contralateral arm, with more than 80% of stroke survivors experiencing this condition in the acute phase, and only half regaining some useful upper limb function after six months. Within the European project RETRAINER (grant agreement No 644721), the consortium developed a platform for the rehabilitation of the upper limb after stroke, which combines a passive arm exoskeleton for weight relief supporting both shoulder and elbow movements, Functional Electrical Stimulation (FES) of the two-most impaired muscles of the affected side, interactive objects, and voluntary effort. The system also provides a graphical user interface which helps the therapist set the training session and save the training data and parameters, and provides the subject a visual feedback about his/her active involvement in the exercise. The training consists of the execution of a series of exercises involving the affected arm during daily life activities. Typical exercises are anterior reaching on a plane or in the space, moving an object on a plane or in the space, moving the hand to the mouth, with or without an object in the hand, and lateral elevation of the shoulder. The aim of this clinical study it to evaluate the efficacy of this novel training platform on patients between two weeks and nine months after their first stroke, who preserved at least a visible muscle contraction for the arm and shoulder muscles. Participants are randomized in an experimental and a control group. The control group is trained with an advanced rehabilitative program, including physical training, occupational therapy, FES, and virtual reality, while the experimental group is trained with the RETRAINER system for about 30 minutes, in addition to the same program of the control group. The daily training time is the same for the t