L Zollo1, E Gallotta, E Guglielmelli, S Sterzi. 1. Laboratory of Biomedical Robotics and Biomicrosystems, Università Campus Bio-Medico, Rome, Italy. l.zollo@unicampus.it
Abstract
BACKGROUND: The use of robotic technology for assessment has the potential to provide therapists with objective, accurate, repeatable measurements of subject's functions. However, despite the increasing number of clinical studies examining the effect of robotic training on stroke rehabilitation, body functions and structures assessment is typically carried out through traditional human-administered clinical impairment scales. AIM: The paper aims at providing a complete set of kinematic and dynamic indices for an objective measure of the effect of robot-aided therapy, and testing their correlation with clinical scales. DESIGN: An intervention pilot study applying robotic therapy was carried out. SETTING: The clinical study was focused on outpatients and was carried out at Università Campus Bio-Medico of Rome, Italy. POPULATION: Fifteen community-dwelling persons with chronic stroke met inclusion criteria and volunteered to participate. METHODS: Upper limb robotic therapy was administered to patients. Kinematic and dynamic performance indices were extracted from position and force data recorded with the InMotion2 robot. A linear regression analysis was carried out to study correlation with clinical scales to extract a core set of performance indicators. RESULTS: Robotic outcome measures showed a significant improvement of kinematic motor performance; the improvement of dynamic components was significant only in resistive motion and highly correlated with Motor Power. CONCLUSION: Preliminary results showed that arm motor functions and strength of the paretic arm can be objectively measured by means of the proposed bunch of robotic measures. Correlation with Motor Power was high, while correlation with Fugl-Meyer was moderate. CLINICAL REHABILITATION IMPACT: An improvement of clinical body functions assessment is expected in terms of objective, accurate and repeatable measurements of subject's performance during recovery.
BACKGROUND: The use of robotic technology for assessment has the potential to provide therapists with objective, accurate, repeatable measurements of subject's functions. However, despite the increasing number of clinical studies examining the effect of robotic training on stroke rehabilitation, body functions and structures assessment is typically carried out through traditional human-administered clinical impairment scales. AIM: The paper aims at providing a complete set of kinematic and dynamic indices for an objective measure of the effect of robot-aided therapy, and testing their correlation with clinical scales. DESIGN: An intervention pilot study applying robotic therapy was carried out. SETTING: The clinical study was focused on outpatients and was carried out at Università Campus Bio-Medico of Rome, Italy. POPULATION: Fifteen community-dwelling persons with chronic stroke met inclusion criteria and volunteered to participate. METHODS: Upper limb robotic therapy was administered to patients. Kinematic and dynamic performance indices were extracted from position and force data recorded with the InMotion2 robot. A linear regression analysis was carried out to study correlation with clinical scales to extract a core set of performance indicators. RESULTS: Robotic outcome measures showed a significant improvement of kinematic motor performance; the improvement of dynamic components was significant only in resistive motion and highly correlated with Motor Power. CONCLUSION: Preliminary results showed that arm motor functions and strength of the paretic arm can be objectively measured by means of the proposed bunch of robotic measures. Correlation with Motor Power was high, while correlation with Fugl-Meyer was moderate. CLINICAL REHABILITATION IMPACT: An improvement of clinical body functions assessment is expected in terms of objective, accurate and repeatable measurements of subject's performance during recovery.
Authors: Zachary A Wright; Emily Lazzaro; Kelly O Thielbar; James L Patton; Felix C Huang Journal: IEEE Trans Neural Syst Rehabil Eng Date: 2017-10-16 Impact factor: 3.802