4.8 Article

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

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NATURE COMMUNICATIONS
卷 11, 期 1, 页码 -

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NATURE PUBLISHING GROUP
DOI: 10.1038/s41467-020-15990-7

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资金

  1. Agency for Science, Technology and Research (A*STAR) under its AME Programmatic Funding Scheme [A18A1b0045]
  2. National Research Foundation (NRF), Prime Minister's office, Singapore, under its NRF Investigatorship [NRF-NRFI2017-07]
  3. Singapore Ministry of Education Tier 2 [MOE2017T2-2-107]
  4. Japan Society for the Promotion of Science Overseas Research Fellowships

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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 similar to 34 and surface electromyogram with a signal-to-noise ratio of 32.2dB. 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. Designing efficient systems capable emulating the muscular excitation-contraction signatures, remains a challenge. Here, the authors propose cytoadhesion-inspired hybrids as locally-coupled electromechanical interfaces capable retrieving the myoelectric and mechanical signals.

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