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. 2022 Feb 17;19:21. doi: 10.1186/s12984-022-01000-y

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© The Author(s) 2022

Open AccessThis 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

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Fig. 2 — A Example of a real-time perturbation detection based on the detection algorithm described in Perturbation detection section. Vertical dark grey area represents the duration of the perturbation, and light yellow area the interval in which the robotic assistance was being triggered. B Detection times obtained during the experiment performed with 10 healthy subjects (mean ± SD), who received perturbations of two magnitudes (Low and High) at two moments of the gait cycle (DSRF double support with right leg in front, SSR single support right leg)