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dc.contributor.author
Sommerhalder, Michael
dc.contributor.author
Zimmermann, Yves
dc.contributor.author
Simovic, Leonardo
dc.contributor.author
Hutter, Marco
dc.contributor.author
Wolf, Peter
dc.contributor.author
Riener, Robert
dc.date.accessioned
2023-10-06T11:07:41Z
dc.date.available
2023-10-04T07:32:09Z
dc.date.available
2023-10-06T11:07:41Z
dc.date.issued
2023-10
dc.identifier.issn
2377-3766
dc.identifier.other
10.1109/LRA.2023.3311229
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/634922
dc.description.abstract
Robot-assisted neurorehabilitation requires trajectories between arbitrary poses in the patient's range of motion. Data-driven optimization methods, such as Learning by Demonstration, are well suited to replicate complex multi-joint movements. However, these methods lack individualization to patient-, robot- and exercise-specific constraints. We propose a hybrid optimization framework that combines cost-based objectives, such as minimizing jerk, with the data-driven optimization of a reference trajectory. The objectives can be individually weighted in a sequential quadratic program with application-related constraints represented in intuitive workspaces. We demonstrated that trajectories recorded from an existing upper-limb activity dataset could be adapted to the personal needs of a healthy participant with simulated impairments, the hardware-specific robot topology, and changes in the exercise setup. Furthermore, we showed how redundancies in the degrees of freedom of the arm can be exploited: For example, an elbow angle movement of 30.4${^\circ }$ was compensated entirely through increased wrist movement in a reach-goal task. In addition to making sequential quadratic programming more accessible to the field of rehabilitation robotics, our framework improves the variability and individualizability of generated trajectories for patients, provides more adaptation possibilities to the therapist, and enables sharing of recorded movement data between robotic platforms, patients, and exercises.
en_US
dc.language.iso
en
en_US
dc.publisher
IEEE
en_US
dc.subject
Rehabilitation robotics
en_US
dc.subject
motion and path planning
en_US
dc.subject
Physical human-robot interaction
en_US
dc.subject
physically assistive devices
en_US
dc.title
Trajectory Optimization Framework for Rehabilitation Robots with Multi-Workspace Objectives and Constraints
en_US
dc.type
Journal Article
dc.date.published
2023-09-04
ethz.journal.title
IEEE Robotics and Automation Letters
ethz.journal.volume
8
en_US
ethz.journal.issue
10
en_US
ethz.pages.start
6819
en_US
ethz.pages.end
6826
en_US
ethz.identifier.wos
ethz.identifier.scopus
ethz.publication.place
New York, NY
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02070 - Dep. Gesundheitswiss. und Technologie / Dep. of Health Sciences and Technology::03654 - Riener, Robert / Riener, Robert
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02070 - Dep. Gesundheitswiss. und Technologie / Dep. of Health Sciences and Technology::03654 - Riener, Robert / Riener, Robert
ethz.date.deposited
2023-10-04T07:32:11Z
ethz.source
SCOPUS
ethz.eth
yes
en_US
ethz.availability
Metadata only
en_US
ethz.rosetta.installDate
2023-10-06T11:07:42Z
ethz.rosetta.lastUpdated
2024-02-03T04:30:57Z
ethz.rosetta.versionExported
true
ethz.COinS
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