Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/41041
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dc.contributor.advisorMEYNS, Pieter
dc.contributor.advisorVAN DEN BOGAART, Maud
dc.contributor.authorVan Herck, Quinten
dc.contributor.authorBriers, Miel
dc.date.accessioned2023-09-21T07:48:01Z-
dc.date.available2023-09-21T07:48:01Z-
dc.date.issued2023
dc.identifier.urihttp://hdl.handle.net/1942/41041-
dc.format.mimetypeApplication/pdf
dc.languagenl
dc.publisherUHasselt
dc.titleValidity of deep learning-based 2D markerless motion capture for measuring joint centres in the sagittal plane during walking and jogging conditions
dc.typeTheses and Dissertations
local.bibliographicCitation.jcatT2
dc.description.notesmaster in de revalidatiewetenschappen en de kinesitherapie-revalidatiewetenschappen en kinesitherapie bij musculoskeletale aandoeningen
local.type.specifiedMaster thesis
item.fulltextWith Fulltext-
item.contributorVan Herck, Quinten-
item.contributorBriers, Miel-
item.fullcitationVan Herck, Quinten & Briers, Miel (2023) Validity of deep learning-based 2D markerless motion capture for measuring joint centres in the sagittal plane during walking and jogging conditions.-
item.accessRightsOpen Access-
Appears in Collections:Master theses
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