Descriptive Study on the Influence of Intermediation Methods Using Robots on the Improvement of Upper Extremity in Stroke Patients

Upper extremity dysfunction, which occurs after stroke, acts as a major cause of obstruction of motion, such as elaborate hand gestures, manipulation, eating, writing, personal hygiene control, expression of opinion, walking and balancing, etc., thus hindering the social independence of patients and causing poor quality of life. Restoration of upper limb function in stroke patients can be said to be important in maintaining the most basic human life. The walking function of the lower extremities is as important as the walking function of the lower extremities in carrying out daily life. In this study, when patient-centered robot assisted rehabilitation was applied to patients with subacute stroke through setting demands and goals for daily life, and motion analysis, it was positive not only to improve the patient’s upper body function, but also to improve the performance of daily activities rather than robot-centered robot assisted rehabilitation with the focus of existing robot devices. In addition, patient-centered robot assisted rehabilitation and robot-centered robotic rehabilitation were more effective than traditional rehabilitation in the range of joint operation of the upper distal region, grip of the hand, and grip strength. On the other hand, patient-centered robot assisted rehabilitation and traditional rehabilitation showed more positive effects than robot-centered robot assisted rehabilitation. Based on these findings, it is meaningful that the research provided a basis for applying patient-centered robotic rehabilitation to improve upper limb function and performance of daily operations in subacute stroke patients. Abstract data (average age, the number of patients in experiment and control groups, the average change score for motor recovery and function level measurements, and the standard deviation of experiment and control groups in criteria) were entered into Excel for Windows. For all the resulting variables, the threshold for rejecting the H 0 hypothesis was set to P < 0.05 (2-tailed)