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. 2020 Apr 10;10:6202. doi: 10.1038/s41598-020-63125-1

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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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Feature importance for the classification of emotion. Two decision tree classifiers were trained and tested with the eight computed features as predictors and the four emotional categories as the predicted class. Kinematic features included velocity, acceleration and vertical movement. Postural features included limb angles, symmetry, shoulder ratio, surface and limb contraction. (a) Predictor relevance for the classification model where the postural and kinematic features were averaged over time (overall classification accuracy of 61%, with chance level at 25%); (b) Predictor relevance for the classification model where the postural and kinematic features kept the temporal information (overall classification accuracy of 84%, with chance level at 25%).