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A Novel End-Effector Robot System Enabling to Monitor Upper-Extremity Posture During Robot-Aided Planar Reaching Movements

Yeji Hwang, Seongpung Lee, Jaesung Hong, Jonghyun Kim

Year
2020
Citations
9

Abstract

End-effector type robots have been popularly applied to robot-aided therapy for rehabilitation purpose. However, those robots have a key drawback for the purpose: lack of the user's posture (joint angle) information. This letter proposes a novel end-effector rehabilitation robot system that contains a contactless motion sensor to monitor upper-extremity posture during robot-aided reaching exercise. The sensor allows the posture estimation without complicated procedures but has an inaccuracy problem such as occlusion and an unreliable segment length. Therefore, we developed a posture monitoring method, which is an analytical method without training procedure, based on the combined use of the information obtained from the sensor and the robot. Eight healthy subjects participated in the experiment with planar reaching exercise for validation. The results of joint angle estimation, high correlation coefficient (0.95 ± 0.03) and small errors (3.55 ± 0.70 deg), show that the proposed system can provide affordable upper-extremity posture estimation.

Keywords

RobotRobot end effectorComputer scienceArtificial intelligenceComputer visionPlanarSimulation

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