Locally coupled electromechanical interfaces based on cytoadhesion-inspired hybrids to identify muscular excitation-contraction signatures

Pingqiang Cai, Changjin Wan, Liang Pan, Naoji Matsuhisa, Ke He, Zequn Cui, Wei Zhang, Chengcheng Li, Jianwu Wang, Jing Yu, Ming Wang, Ying Jiang, Geng Chen, Xiaodong Chen

Research output: Contribution to journalArticle

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.

Original languageEnglish
Article number2183
JournalNature communications
Volume11
Issue number1
DOIs
Publication statusPublished - 2020 Dec 1

ASJC Scopus subject areas

  • Chemistry(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Physics and Astronomy(all)

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    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://doi.org/10.1038/s41467-020-15990-7