Feedforward model based arm weight compensation with the rehabilitation robot ARMin

Just, Fabian; Özen, Özhan; Tortora, Stefano; Riener, Robert; Rauter, Georg (15 August 2017). Feedforward model based arm weight compensation with the rehabilitation robot ARMin. In: 2017 International Conference on Rehabilitation Robotics (ICORR) (pp. 72-77). IEEE 10.1109/ICORR.2017.8009224

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Highly impaired stroke patients at early stages of recovery are unable to generate enough muscle force to lift the weight of their own arm. Accordingly, task-related training is strongly limited or even impossible. However, as soon as partial or full arm weight support is provided, patients are enabled to perform arm rehabilitation training again throughout an increased workspace. In the literature, the current solutions for providing arm weight support are mostly mechanical. These systems have components that restrict the freedom of movement or entail additional disturbances. A scalable weight compensation for upper and lower arm that is online adjustable as well as generalizable to any robotic system is necessary. In this paper, a model-based feedforward weight compensation of upper and lower arm fulfilling these requirements is introduced. The proposed method is tested with the upper extremity rehabilitation robot ARMin V, but can be applied in any other actuated exoskeleton system. Experimental results were verified using EMG measurements. These results revealed that the proposed weight compensation reduces the effort of the subjects to 26% on average and more importantly throughout the entire workspace of the robot.

Item Type:

Conference or Workshop Item (Paper)

Division/Institute:

10 Strategic Research Centers > ARTORG Center for Biomedical Engineering Research > ARTORG Center - Motor Learning and Neurorehabilitation
10 Strategic Research Centers > ARTORG Center for Biomedical Engineering Research > ARTORG Center - Gerontechnology and Rehabilitation

UniBE Contributor:

Özen, Özhan and Riener, Robert

Subjects:

600 Technology > 610 Medicine & health
600 Technology > 620 Engineering

ISBN:

978-1-5386-2296-4

Publisher:

IEEE

Language:

English

Submitter:

Angela Amira Botros

Date Deposited:

19 Jul 2018 14:31

Last Modified:

22 Oct 2019 19:30

Publisher DOI:

10.1109/ICORR.2017.8009224

BORIS DOI:

10.7892/boris.117067

URI:

https://boris.unibe.ch/id/eprint/117067

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