Data Fusion-Based Musculoskeletal Synergies in the Grasping Hand

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dc.contributor.authorOlikkal, Parthan Sathishkumar
dc.contributor.authorPei, Dingyi
dc.contributor.authorAdali, Tulay
dc.contributor.authorBanerjee, Nilanjan
dc.contributor.authorVinjamuri, Ramana
dc.date.accessioned2023-06-06T13:19:32Z
dc.date.available2023-06-06T13:19:32Z
dc.date.issued2022-09-29
dc.description.abstractThe hypothesis that the central nervous system (CNS) makes use of synergies or movement primitives in achieving simple to complex movements has inspired the investigation of different types of synergies. Kinematic and muscle synergies have been extensively studied in the literature, but only a few studies have compared and combined both types of synergies during the control and coordination of the human hand. In this paper, synergies were extracted first independently (called kinematic and muscle synergies) and then combined through data fusion (called musculoskeletal synergies) from 26 activities of daily living in 22 individuals using principal component analysis (PCA) and independent component analysis (ICA). By a weighted linear combination of musculoskeletal synergies, the recorded kinematics and the recorded muscle activities were reconstructed. The performances of musculoskeletal synergies in reconstructing the movements were compared to the synergies reported previously in the literature by us and others. The results indicate that the musculoskeletal synergies performed better than the synergies extracted without fusion. We attribute this improvement in performance to the musculoskeletal synergies that were generated on the basis of the cross-information between muscle and kinematic activities. Moreover, the synergies extracted using ICA performed better than the synergies extracted using PCA. These musculoskeletal synergies can possibly improve the capabilities of the current methodologies used to control high dimensional prosthetics and exoskeletons.en_US
dc.description.sponsorshipThis research was funded by National Science Foundation (NSF) CAREER Award, grant number HCC-2053498 and NSF Planning IUCRC Award, grant number 2042203.en_US
dc.description.urihttps://www.mdpi.com/1424-8220/22/19/7417en_US
dc.format.extent16 pagesen_US
dc.genrejournal articlesen_US
dc.identifierdoi:10.13016/m232sw-6qxh
dc.identifier.citationOlikkal, Parthan, Dingyi Pei, Tülay Adali, Nilanjan Banerjee, and Ramana Vinjamuri. 2022. "Data Fusion-Based Musculoskeletal Synergies in the Grasping Hand" Sensors 22, no. 19: 7417. https://doi.org/10.3390/s22197417en_US
dc.identifier.urihttps://doi.org/10.3390/s22197417
dc.identifier.urihttp://hdl.handle.net/11603/28107
dc.language.isoen_USen_US
dc.publisherMDPIen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Computer Science and Electrical Engineering Department Collection
dc.relation.ispartofUMBC Faculty Collection
dc.relation.ispartofUMBC Student Collection
dc.rightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.en_US
dc.rightsAttribution 4.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/us/*
dc.titleData Fusion-Based Musculoskeletal Synergies in the Grasping Handen_US
dc.typeTexten_US
dcterms.creatorhttps://orcid.org/0000-0002-5513-1150en_US
dcterms.creatorhttps://orcid.org/0000-0001-7756-3678en_US
dcterms.creatorhttps://orcid.org/0000-0003-0594-2796en_US
dcterms.creatorhttps://orcid.org/0000-0003-1650-5524en_US

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