The development of robotic devices for rehabilitation is a fast-growing field. Nowadays, thanks to novel technologies that have improved robots' capabilities and offered more cost-effective solutions, robotic devices are increasingly being employed during clinical practice, with the goal of boosting patients' recovery. Robotic rehabilitation is also widely used in the context of neurological disorders, where it is often provided in a variety of different fashions, depending on the specific function to be restored. Indeed, the effect of robot-aided neurorehabilitation can be maximized when used in combination with a proper training regimen (based on motor control paradigms) or with non-invasive brain machine interfaces. Therapy-induced changes in neural activity and behavioral performance, which may suggest underlying changes in neural plasticity, can be quantified by multimodal assessments of both sensorimotor performance and brain/muscular activity pre/post or during intervention. Here, we provide an overview of the most common robotic devices for upper and lower limb rehabilitation and we describe the aforementioned neurorehabilitation scenarios. We also review assessment techniques for the evaluation of robotic therapy. Additional exploitation of these research areas will highlight the crucial contribution of rehabilitation robotics for promoting recovery and answering questions about reorganization of brain functions in response to disease.In the last decades, innovative robotic technologies have been developed in order to effectively help clinicians during the neurorehabilitation process. The term "robotic technology" in this application domain refers to any mechatronic device with a certain degree of intelligence that can physically intervene on the behavior of the patient, optimizing and speeding up his/her sensorimotor recovery. The two key capabilities of these robots are: (1) Assessing the human sensorimotor function; and (2) re-training the human brain in order to improve the patient's quality of life. However, most of the studies in this field have been focused more on the development of the devices, whereas less effort was made on maximizing their efficacy for promoting recovery. The main challenge consists of designing effective training modalities, supported by appropriate control strategies. Thus, each robotic device supports a pre-defined training modality depending on the low-level control strategy implemented and also on the residual abilities of each patient. Usually, most of the rehabilitation devices implement a passive training modality (robot-driven, position control strategy), where the robot imposes the trajectories, and an active training modality (patient-driven), where the robot modulates its trajectory in response to the subject's intention to move [7,8]. However, among all the different training modalities, the most relevant is the assistive one. Assistive controllers help participants to move their impaired limbs according to the desired postures during grasping, reaching, or walki...

This study investigated how stroke’s hemispheric localization affects motor performance, spinal maps and muscle synergies while performing planar reaching with and without assistive or resistive forces. A lesion of the right hemisphere affected performance, reducing average speed and smoothness and augmenting lateral deviation in both arms. Instead, a lesion of the left hemisphere affected the aiming error, impairing the feedforward control of the ipsilesional arm. The structure of the muscle synergies had alterations dependent on the lesion side in both arms. The applied force fields reduced the differences in performance and in muscle activations between arms and among populations. These results support the hypotheses of hemispheric specialization in movement control and identify potential significant biomarkers for the design of more effective and personalized rehabilitation protocols.

Proprioception is a crucial sensory modality involved in the control and regulation of coordinated movements and in motor learning. However, the extent to which proprioceptive acuity is influenced by local muscle fatigue is obscured by methodological differences in proprioceptive and fatiguing protocols. In this study, we used high resolution kinematic measurements provided by a robotic device, as well as both frequency and time domain analysis of signals captured via surface electromyography (sEMG) to examine the effects of local muscle fatigue on wrist proprioceptive acuity in 16 physically and neurologically healthy young adults. To this end, participants performed a flexion/extension ipsilateral joint position matching test (JPM), after which a high-resistive robotic task was used to induce muscle fatigue of the flexor carpi radialis (FCR) muscle. The JPM test was then repeated in order to analyze potential changes in proprioceptive acuity. Results indicated that the fatigue protocol had a significant effect on movements performed in flexion direction, with participants exhibiting a tendency to undershoot the target before the fatigue protocol (−1.218°), but overshooting after the fatigue protocol (0.587°). In contrast, in the extension direction error bias values were similar before and after the fatigue protocol as expected (pre = −1.852°, post = −1.237°) and reflected a tendency to undershoot the target. Moreover, statistical analysis indicated that movement variability was not influenced by the fatigue protocol or movement direction. In sum, results of the present study demonstrate that an individual’s estimation of wrist joint displacement (i.e., error bias), but not precision (i.e., variability), is affected by muscular fatigue in a sample of neurologically and physically healthy adults.