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dc.contributor.authorGroos, Daniel
dc.contributor.authorAdde, Lars
dc.contributor.authorStøen, Ragnhild
dc.contributor.authorRamampiaro, Heri
dc.contributor.authorIhlen, Espen Alexander F.
dc.description.abstractAssessment of spontaneous movements can predict the long-term developmental disorders in high-risk infants. In order to develop algorithms for automated prediction of later disorders, highly precise localization of segments and joints by infant pose estimation is required. Four types of convolutional neural networks were trained and evaluated on a novel infant pose dataset, covering the large variation in 1424 videos from a clinical international community. The localization performance of the networks was evaluated as the deviation between the estimated keypoint positions and human expert annotations. The computational efficiency was also assessed to determine the feasibility of the neural networks in clinical practice. The best performing neural network had a similar localization error to the inter-rater spread of human expert annotations, while still operating efficiently. Overall, the results of our study show that pose estimation of infant spontaneous movements has a great potential to support research initiatives on early detection of developmental disorders in children with perinatal brain injuries by quantifying infant movements from video recordings with human-level performance.en_US
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internasjonal*
dc.titleTowards human-level performance on automatic pose estimation of infant spontaneous movementsen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.source.journalComputerized Medical Imaging and Graphicsen_US

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Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal
Med mindre annet er angitt, så er denne innførselen lisensiert som Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal