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. 2020 Mar 9;10:4372. doi: 10.1038/s41598-020-60932-4

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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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R2 difference with UKF between (1) Downsample vs Delta and (2) All frequency vs Delta. Each row represents different joint angles for the right leg and each column represents the differences between (1) Experiment 2 vs (1, 2) Experiment 3 vs 1 in performance (R2 score). The black line shows a zero threshold to indicate which experiment performed better at a certain tap sizes. Similar algorithms were grouped using shapes. The errorbar shows 25th to 75th percentile and the middle marker shows the median value. The inset figures show a magnified view to compare different algorithms other than the UKF. Linear algorithms with a circle (LR: Linear Regression, RR: Ridge Regression), Adaptive filter with a square (UKF: Unscented Kalman Filter), Boosting with a cross (CB: CatBoost), CNN with a triangle (TCN: Temporal Convolutional Network), RNN with a diamond (LSTM: Long Short Term Memory, GRU: Gated Recurrent Unit, QRNN: Quasi-Recurrent Neural Network).