Ricerc@Sapienza

Cerebrovascular diseases, including stroke, are historically considered diseases of old adults so only in a few studies has “return to work” (RTW) been considered as an index of rehabilitative outcome. At the moment, data on RTW in patients with stroke are highly variable: four different reviews reported the following ranges: 11–85%, 19–73%, 22–53%, and 40–45%. The absence of re-integration to work after a stroke is shown to be associated with an increase of cardiac disorders and depression, with a higher level of mortality, with social isolation and with insufficient adaptive skills.

Introduction: Robot-assisted therapy is an emerging approach that performs highly repetitive, intensive, task oriented and quantifiable neuro-rehabilitation. In the last decades, it has been increasingly used in a wide range of neurological central nervous system conditions implying an upper limb paresis. Results from the studies are controversial, for the many types of robots and their features often not accompanied by specific clinical indications about the target functions, fundamental for the individualized neurorehabilitation program.

Over the last decades, virtual reality (VR) emerged as a potential tool for developing new rehabilitation treatments in neurological patients. However, despite the increasing number of studies, a clear comprehension about the impact of immersive VR-treatment on balance and posture is still scarce. In the present study, we aimed to investigate the effects of VR cues on balance performances of subjects affected by stroke, age-matched healthy subjects, and young healthy subjects.

Clinical assessment plays a major role in post-stroke rehabilitation programs for evaluating impairment level and tracking recovery progress. Conventionally, this process is manually performed by clinicians using chart-based ordinal scales which can be both subjective and inefficient. In this paper, a novel approach based on fuzzy logic is proposed which automatically evaluates stroke patients’ impairment level using single-channel surface electromyography (sEMG) signals and generates objective classification results based on the widely used Brunnstrom stages of recovery.