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

Recent advances in flexible materials and wearable electronics offer a noninvasive, high-fidelity recording of biopotentials for portable healthcare, disease diagnosis, and machine interfaces. Current device-manufacturing methods, however, still heavily rely on the conventional cleanroom microfabric...

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Veröffentlicht in:ACS applied materials & interfaces Jg. 12; H. 44; S. 49398
Hauptverfasser: Kwon, Young-Tae, Kim, Hojoong, Mahmood, Musa, Kim, Yun-Soung, Demolder, Carl, Yeo, Woon-Hong
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States 04.11.2020
Schlagworte:
Algorithms
Deep Learning
Electronics
Humans
Nanotechnology
Particle Size
Surface Properties
Wearable Electronic Devices
deep learning algorithm
additive nanomanufacturing
printed bioelectronics
electromyograms (EMGs)
human−machine interface
ISSN:1944-8252, 1944-8252
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Zusammenfassung:Recent advances in flexible materials and wearable electronics offer a noninvasive, high-fidelity recording of biopotentials for portable healthcare, disease diagnosis, and machine interfaces. Current device-manufacturing methods, however, still heavily rely on the conventional cleanroom microfabrication that requires expensive, time-consuming, and complicated processes. Here, we introduce an additive nanomanufacturing technology that explores a contactless direct printing of aerosol nanomaterials and polymers to fabricate stretchable sensors and multilayered wearable electronics. Computational and experimental studies prove the mechanical flexibility and reliability of soft electronics, considering direct mounting to the deformable human skin with a curvilinear surface. The dry, skin-conformal graphene biosensor, without the use of conductive gels and aggressive tapes, offers an enhanced biopotential recording on the skin and multiple uses (over ten times) with consistent measurement of electromyograms. The combination of soft bioelectronics and deep learning algorithm allows classifying six classes of muscle activities with an accuracy of over 97%, which enables wireless, real-time, continuous control of external machines such as a robotic hand and a robotic arm. Collectively, the comprehensive study of nanomaterials, flexible mechanics, system integration, and machine learning shows the potential of the printed bioelectronics for portable, smart, and persistent human-machine interfaces.
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ISSN:1944-8252
1944-8252
DOI:10.1021/acsami.0c14193