Computational modeling and simulation of closed chain arm-robot multibody dynamic systems in OpenSim

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dc.citation.firstpage313en_US
dc.citation.journalTitleMultibody System Dynamicsen_US
dc.citation.lastpage334en_US
dc.citation.volumeNumber56en_US
dc.contributor.authorGreen, Matthewen_US
dc.contributor.authorHong, Yoon No Gregoryen_US
dc.contributor.authorRoh, Jinsooken_US
dc.contributor.authorFregly, Benjamin J.en_US
dc.date.accessioned2022-12-13T19:11:32Zen_US
dc.date.available2022-12-13T19:11:32Zen_US
dc.date.issued2022en_US
dc.description.abstractRehabilitation robot efficacy for restoring upper extremity function post-stroke could potentially be improved if robot control algorithms accounted for patient-specific neural control deficiencies. As a first step toward the development of such control algorithms using model-based methods, this study provides general guidelines for creating and simulating closed chain arm-robot models in the OpenSim environment, along with a specific example involving a three-dimensional arm moving within a two degree-of-freedom upper extremity rehabilitation robot. The closed chain arm-robot model developed in OpenSim was evaluated using experimental robot motion and torque data collected from a single healthy subject under four conditions: 1) active robot alone, 2) active robot with passive arm, 3) passive robot with active arm, and 4) active robot with active arm. Computational verification of the combined model was performed for all four conditions, whereas experimental validation was performed for only the first two conditions since torque measurements were not available for the arm. For the four verification problems, forward dynamic simulations reproduced experimentally measured robot joint angles with average root-mean-square (RMS) errors of less than 0.3 degrees and correlation coefficients of 1.00. For the two validation problems, inverse dynamic simulations reproduced experimentally measured robot motor torques with average RMS errors less than or equal to 0.5 Nm and correlation coefficients between 0.92 and 0.99. If patient-specific muscle–tendon and neural control models can be successfully added in the future, the coupled arm-robot OpenSim model may provide a useful testbed for designing patient-specific robot control algorithms that facilitate recovery of upper extremity function post-stroke.en_US
dc.identifier.citationGreen, Matthew, Hong, Yoon No Gregory, Roh, Jinsook, et al.. "Computational modeling and simulation of closed chain arm-robot multibody dynamic systems in OpenSim." <i>Multibody System Dynamics,</i> 56, (2022) Springer Nature: 313-334. https://doi.org/10.1007/s11044-022-09847-8.en_US
dc.identifier.digitals11044-022-09847-8en_US
dc.identifier.doihttps://doi.org/10.1007/s11044-022-09847-8en_US
dc.identifier.urihttps://hdl.handle.net/1911/114117en_US
dc.language.isoengen_US
dc.publisherSpringer Natureen_US
dc.rightsThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.en_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.titleComputational modeling and simulation of closed chain arm-robot multibody dynamic systems in OpenSimen_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpublisher versionen_US
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