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. 2020 May 4;11:2183. doi: 10.1038/s41467-020-15990-7

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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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Fig. 6 — a Schematic shows the process of identifying human grip signatures for manipulating robotic grips. Both human and robotic grips involve two steps, namely the minFist and tightening. The coupled electromechanical patterns retrieved by CoupOn can orthogonally recognize the speed and strength of human grips, therefore allowing the categorization of four permutations in the gesture matrix, according to speed of the two steps. Scale bar, 2 cm. b Plots show the retrieved excitation–contraction signatures corresponding to the four permutations in gesture matrix. Data obtained on subject 5. c Photograph shows the setup for the robotic grip of a soft yellow ball with the ring finger connected with a force gauge. d Plots indicate the grip force exerted on the soft ball during the four permutations of recapitulated grips.