Background: Stroke is one of the leading causes of adult disability, and up to 80% of stroke survivors undergo upper extremity motor dysfunction. Constraint-Induced Movement Therapy (CIMT) and Robot-Assisted Therapy (RT) are used for upper limb stroke rehabilitation. Although CIMT and RT are different techniques, both are beneficial; however, their results must be compared. The objective is to establish the difference between RT and CIMT after a rehabilitation program for chronic stroke patients.Method: This is a randomized clinical trial, registered at ClinicalTrials.gov (ID number NCT02700061), in which patients with stroke received sessions of RT or CIMT protocol, combined with a conventional rehabilitation program for 12 weeks. The primary outcome was measured by Wolf Motor Function Test (WMFT) and Fugl-Meyer Assessment—Upper Limb (FMA-UL). Activities of daily living were also assessed.Results: Fifty one patients with mild to moderate upper limb impairment were enrolled in this trial, 25 women and 26 men, mean age of 60,02 years old (SD 14,48), with 6 to 36 months after stroke onset. Function significantly improved regardless of the treatment group. However, no statistical difference was found between both groups as p-values of the median change of function measured by WMFT and FMA were 0.293 and 0.187, respectively.Conclusion: This study showed that Robotic Therapy (RT) was not different from Constraint-Induced Movement Therapy (CIMT) regardless of the analyzed variables. There was an overall upper limb function, motor recovery, functionality, and activities of daily living improvement regardless of the interventions. At last, the combination of both techniques should be considered in future studies.
Aiming to perform an extraction of features which are strongly related to hemiparesis, this work describes a case study involving the efforts of patients in upper-limb rehabilitation, diagnosed with such pathology. Expressed as data (kinematic and dynamic measures), patients' performance were sensed and stored by a single InMotion Arm robotic device for further analysis. It was applied a Knowledge Discovery roadmap over collected data in order to preprocess, transform and perform data mining through machine learning methods. Our efforts culminated in a pattern classification with the abilty to distinguish hemiparetic sides with an accuracy rate of 94%, having 8 features of rehabilitation performance feeding the input. Interpreting the obtained feature structure, it was observed that force-related attributes are more significant to the composition of the extracted pattern
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