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dc.contributor.authorKhodadadzadeh, Massoud
dc.contributor.authorSloan, Aliza T.
dc.contributor.authorJones, Nancy Aaron
dc.contributor.authorCoyle, Damien
dc.contributor.authorKelso, J.A. Scott
dc.identifier.citationKhodadadzadeh M, Sloan AT, Jones NA, Coyle D, Kelso JAS (2024) 'Artificial intelligence detects awareness of functional relation with the environment in 3 month old babies', Scientific Reports, 14 (1), 15580.en_US
dc.description.abstractA recent experiment probed how purposeful action emerges in early life by manipulating infants' functional connection to an object in the environment (i.e., tethering an infant's foot to a colorful mobile). Vicon motion capture data from multiple infant joints were used here to create Histograms of Joint Displacements (HJDs) to generate pose-based descriptors for 3D infant spatial trajectories. Using HJDs as inputs, machine and deep learning systems were tasked with classifying the experimental state from which snippets of movement data were sampled. The architectures tested included k-Nearest Neighbour (kNN), Linear Discriminant Analysis (LDA), Fully connected network (FCNet), 1D-Convolutional Neural Network (1D-Conv), 1D-Capsule Network (1D-CapsNet), 2D-Conv and 2D-CapsNet. Sliding window scenarios were used for temporal analysis to search for topological changes in infant movement related to functional context. kNN and LDA achieved higher classification accuracy with single joint features, while deep learning approaches, particularly 2D-CapsNet, achieved higher accuracy on full-body features. For each AI architecture tested, measures of foot activity displayed the most distinct and coherent pattern alterations across different experimental stages (reflected in the highest classification accuracy rate), indicating that interaction with the world impacts the infant behaviour most at the site of organism~world connection.en_US
dc.description.sponsorshipWe are grateful for access to the Tier 2 High Performance Computing resources provided by the Northern Ireland High-Performance Computing (NI-HPC) facility funded by the UK Engineering and Physical Sciences Research Council (EPSRC), Grant Nos. EP/T022175/ and EP/W03204X/1. DC is grateful for the UKRI Turing AI Fellowship 2021-2025 funded by the EPSRC (grant number EP/V025724/1). M.K. is supported by a Vice Chancellor’s Research Scholarship and EPSRC (grant number EP/V025724/1) and the Institute for Research in Applicable Computing (IRAC) at the University of Bedfordshire. J.A.S.K and A.T.S were supported by The FAU Foundation (Eminent Scholar in Science) and National Institute of Health (MH 08038).en_US
dc.rightsGreen - can archive pre-print and post-print or publisher's version/PDF
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.subjectartificial intelligenceen_US
dc.subjectSubject Categories::G700 Artificial Intelligenceen_US
dc.titleArtificial intelligence detects awareness of functional relation with the environment in 3 month old babiesen_US
dc.contributor.departmentUniversity of Bedfordshireen_US
dc.contributor.departmentUniversity of Bathen_US
dc.contributor.departmentUlster Universityen_US
dc.contributor.departmentFlorida Atlantic Universityen_US
dc.identifier.journalScientific Reportsen_US
dc.description.notegold oa

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