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Printed, Wireless, Soft Bioelectronics and Deep Learning Algorithm for Smart Human-Machine Interfaces.

Young-Tae Kwon1, Hojoong Kim1, Musa Mahmood1

  • 1George W. Woodruff School of Mechanical Engineering, Institute for Electronics and Nanotechnology, Georgia Institute of Technology, Atlanta, Georgia 30332, United States.

ACS Applied Materials & Interfaces
|October 21, 2020
PubMed
Summary
This summary is machine-generated.

We developed a new printing method for flexible electronics, enabling high-quality, wearable biosensors for healthcare and machine control. This technology offers a simpler, more accessible way to create advanced human-machine interfaces.

Keywords:
additive nanomanufacturingdeep learning algorithmelectromyograms (EMGs)human−machine interfaceprinted bioelectronics

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Area of Science:

  • Materials Science
  • Biomedical Engineering
  • Nanotechnology

Background:

  • Conventional microfabrication of wearable electronics is costly and complex.
  • Advances in flexible materials enable noninvasive biopotential recording for healthcare and machine interfaces.

Purpose of the Study:

  • To introduce an additive nanomanufacturing technology for fabricating stretchable sensors and wearable electronics.
  • To demonstrate the mechanical reliability and skin-conformality of printed soft electronics.
  • To showcase the potential of printed bioelectronics for advanced human-machine interfaces.

Main Methods:

  • Utilized contactless direct printing of aerosol nanomaterials and polymers.
  • Fabricated stretchable sensors and multilayered wearable electronics.
  • Validated mechanical flexibility and reliability through computational and experimental studies.
  • Integrated soft bioelectronics with a deep learning algorithm.

Main Results:

  • Developed dry, skin-conformal graphene biosensors for enhanced biopotential recording.
  • Achieved consistent electromyogram measurements with over ten uses.
  • Classified six classes of muscle activities with >97% accuracy using deep learning.
  • Enabled wireless, real-time control of external machines like robotic limbs.

Conclusions:

  • Additive nanomanufacturing offers a simpler alternative to cleanroom fabrication for wearable electronics.
  • Printed bioelectronics demonstrate high performance and reliability for skin-based sensing.
  • The integration of printed bioelectronics and AI enables sophisticated human-machine interfaces.