Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/147436
Title: Locally coupled electromechanical interfaces based on cytoadhesion-inspired hybrids to identify muscular excitation-contraction signatures
Authors: Cai, Pingqiang
Wan, Changjin
Pan, Liang
Matsuhisa, Naoji
He, Ke
Cui, Zequn
Zhang, Wei
Li, Chengcheng
Wang, Jianwu
Yu, Jing
Wang, Ming
Jiang, Ying
Chen, Geng
Chen, Xiaodong
Keywords: Engineering::Materials
Issue Date: 2020
Source: Cai, P., Wan, C., Pan, L., Matsuhisa, N., He, K., Cui, Z., Zhang, W., Li, C., Wang, J., Yu, J., Wang, M., Jiang, Y., Chen, G. & Chen, X. (2020). Locally coupled electromechanical interfaces based on cytoadhesion-inspired hybrids to identify muscular excitation-contraction signatures. Nature Communications, 11(1), 2183-. https://dx.doi.org/10.1038/s41467-020-15990-7
Journal: Nature communications
Abstract: Coupling myoelectric and mechanical signals during voluntary muscle contraction is paramount in human-machine interactions. Spatiotemporal differences in the two signals intrinsically arise from the muscular excitation-contraction process; however, current methods fail to deliver local electromechanical coupling of the process. Here we present the locally coupled electromechanical interface based on a quadra-layered ionotronic hybrid (named as CoupOn) that mimics the transmembrane cytoadhesion architecture. CoupOn simultaneously monitors mechanical strains with a gauge factor of ~34 and surface electromyogram with a signal-to-noise ratio of 32.2 dB. The resolved excitation-contraction signatures of forearm flexor muscles can recognize flexions of different fingers, hand grips of varying strength, and nervous and metabolic muscle fatigue. The orthogonal correlation of hand grip strength with speed is further exploited to manipulate robotic hands for recapitulating corresponding gesture dynamics. It can be envisioned that such locally coupled electromechanical interfaces would endow cyber-human interactions with unprecedented robustness and dexterity.
URI: https://hdl.handle.net/10356/147436
ISSN: 2041-1723
DOI: 10.1038/s41467-020-15990-7
Rights: © 2020 The Author(s). 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/.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:MSE Journal Articles

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