La Riabilitazione Robot Assistita Del Paziente Neurologico: l'Elettromiografia Dinamica di Superficie Come Strumento Per la Personalizzazione Del Trattamento

Stroke is a leading cause of serious long-term disability in developed countries, and has an enormous emotional and socioeconomic impact on patients, families and, health services. Upper-limb impairments and functional problems are, in fact, very common after a stroke. Impairments commonly include difficulty moving and co-ordinating the arms, hands and fingers, often resulting in difficulty carrying out Activities of Daily Living (ADLs) such as eating, dressing and washing. More than half of people with upper-limb impairment after stroke will still have difficulties in performing ADLs many months to years after their stroke. Robotic rehabilitation systems have the potential to deliver large doses of motor training in a cost-effective manner and, although the debate on the efficacy of robotic therapy is still open, they are emerging as valid solutions to help stroke survivors in the rehabilitation of the upper limb. Recent review of Norouzi-Gheidari shows that the effect of a robotic training is comparable to a conventional therapy training of the same length and intensity. A recent Cochrane systematic review of Mehrholz included 34 trials (involving 1160 participants) showed that electromechanical and robot-assisted arm training improved ADLs scores, arm function, and arm muscle strength, but the quality of the evidence was low to very low. Unfortunately, the mechanisms leading to impairment reduction following the robotic training are still unclear. It is known that neuroplasticity plays an important role in the motor recovery process of stroke patients and, further, that the patient should be engaged during the treatment in order to foster a process similar to motor learning. To promote engagement and maximize neuroplasticity, two main methods have been studied in robotic rehabilitation: i) the assist-as-needed training paradigm, and ii) the Detection of Patient Intent (DPI) method, also called guided force training. The first one, which consists in providing the minimal assistance needed to the subject to complete the task required, has shown promising results in enhancing the participation to the treatment, especially in medium-high functional patients. The DPI method, instead, is based on triggering the movement of the robot using the patient's exerted force or induced velocity. In some cases, the DPI method may even exploit biomedical signals like EMG or EEG to initiate the given task.

Besides the modality of interaction between patient and robot, another important feature that can determining the success of the therapy is the type of movement proposed. It is known that treatments based on purposeful movements show better results in the recovery of the upper-limb function than those based on movements without a goal. Therefore, a proper rehabilitation program should include high repetition task-oriented movements.

Unfortunately, the assist-as-need principle and the DPI method are often of little applicability in training against gravity, especially in the case of low functioning patients with high strength and coordination impairments. In these cases, when the patient is not able to control actively the robot, full assistance, based on a rigidly imposed trajectory (path and motion law), is the only remaining option in robotic rehabilitation.

In this preliminary study, an upper-limb rehabilitation program based on robot fully assisted (rigidly imposed) goal-oriented movements is presented.

Aim of this study is to present a novel robotic approach based on fully assisted functional movements and to examine the effect of the intervention in terms of motor function improvement in subjects with chronic stroke in the short term and at 6-month follow up. A preliminary evaluation of the effectiveness of the intervention in improving activity and participation in the short term is also performed. Further, the study aims to verify whether some instrumental measures (using kinematics, EMG and EEG) may help gain insight into the mechanisms leading to improved motor ability following the robotic intervention and can be used to predict functional recovery.