Development and Validation of a Kinematically Accurate Upper-Limb Exoskeleton Digital Twin for Stroke Rehabilitation

Conference Paper (2023)
Author(s)

Alexandre Ratschat (TU Delft - Human-Robot Interaction, Erasmus MC)

Tiago M. C. Lomba (TU Delft - Cognitive Robotics)

Stefano D. Gasperina (TU Delft - Human-Robot Interaction)

Laura Marchal-Crespo (TU Delft - Human-Robot Interaction, Erasmus MC)

DOI related publication
https://doi.org/10.1109/ICORR58425.2023.10304719 Final published version
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Publication Year
2023
Language
English
Bibliographical Note
Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.
ISBN (print)
979-8-3503-4276-5
ISBN (electronic)
979-8-3503-4275-8
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Abstract

Rehabilitation robotics combined with virtual reality using head-mounted displays enable naturalistic, immersive, and motivating therapy for people after stroke. There is growing interest in employing digital twins in robotic neurore-habilitation, e.g., in telerehabilitation for virtual coaching and monitoring, as well as in immersive virtual reality applications. However, the kinematic matching of the robot's visualization with the real robot movements is hardly validated, potentially affecting the users' experience while immersed in the virtual environment due to a visual-proprioceptive mismatch. The kinematic mismatch may also limit the validity of assessment measures recorded with the digital twin. We present the development and low-cost kinematic validation of a digital twin of a seven active degrees-of-freedom exoskeleton for stroke rehabilitation. We validated the kinematic accuracy of the digital twin end-effector by performing two tasks-a planar reaching task and a 3D functional task-performed by a single healthy participant. We computed the end-effector position and rotation from the forward kinematics of the robot, the digital twin, and data recorded from the real robot using a low-cost tracking system based on HTC VIVE trackers and compared them pair-wise. We found that the digital twin closely matches the forward kinematics and tracked movement of the real robot and thus provides a reliable platform for future research on digital twins for stroke rehabilitation.

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