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. 2020 Jan 13;10:169. doi: 10.1038/s41598-019-56862-5

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© The Author(s) 2020

Open Access This 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Differentiation of high and low motor performance infants with the smart jumpsuit. The plots show individual category distributions (as log-probability) of the given posture and movement category, presented for the entire dataset. Results from both human annotation (x) and the classifier output (triangle) are shown for comparison, and the hairlines connect individuals to assess the individual level reliability. The highlighted recordings correspond to a sample of high performing (red; High perf.) and low performing infants (blue; Low perf.). Rest of the infant cohort is plotted with light gray lines. No statistically significant differences were found between the movement distributions from the human annotations and from classifier outputs (Mann-Whitney U-test, N = 22).