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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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(a) Total cumulative confusion matrices across all possible annotator pairs for the Posture (top) and Movement (bottom) track. The percentages of each column sum up to one and the absolute values denote the number of frames corresponding each cell. (b) t-SNE visualization of the entire dataset based on SVM input features. Color coding is based on the annotations of Posture (top left) and Movement categories (the rest). The visualization of the Movement track has been broken down into 3/3 (top right), 2/3 (bottom right), and 1/3 (bottom left) annotator agreement levels. The ambiguity differences in the annotation accuracies between the tracks can be clearly seen.