The RETRAINER (Reaching and grasping Training based on Robotic hybrid AssIstance for Neurological patients: End users Real life evaluation) project is an Innovation Action funded by the European Commission under the H2020 research framework programme. The project aims at a full technology transfer of the results of a previous FP7 project, MUNDUS, aimed at the development of upper limb assistive technologies, to a robotic system for upper limb and hand rehabilitation to be tested in a wide clinical trial with stroke survivors in two clinical centers. The final result of the project is the design of a validated system suitable to address the rehabilitation market. Along this project’s path, several issues affecting both development and validation have been pointed out and are here summarized to serve as lesson learnt for prospective projects and challenges.

Retrainer project: Perspectives and lesson learnt on clinical trial in rehabilitation robotics to foster industrial exploitation

Pedrocchi, A.;
2019-01-01

Abstract

The RETRAINER (Reaching and grasping Training based on Robotic hybrid AssIstance for Neurological patients: End users Real life evaluation) project is an Innovation Action funded by the European Commission under the H2020 research framework programme. The project aims at a full technology transfer of the results of a previous FP7 project, MUNDUS, aimed at the development of upper limb assistive technologies, to a robotic system for upper limb and hand rehabilitation to be tested in a wide clinical trial with stroke survivors in two clinical centers. The final result of the project is the design of a validated system suitable to address the rehabilitation market. Along this project’s path, several issues affecting both development and validation have been pointed out and are here summarized to serve as lesson learnt for prospective projects and challenges.
2019
CONVERGING CLINICAL AND ENGINEERING RESEARCH ON NEUROREHABILITATION III
978-3-030-01844-3
978-3-030-01845-0
Biomedical Engineering; Mechanical Engineering; Artificial Intelligence
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1076351
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