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Abstract
This paper introduces a novel all-printed, nanomembrane hybrid electronic system (p-NHE) for wireless, multimodal human-machine interfaces (HMIs). The system uses a biocompatible, solderable, functionalized conductive graphene (FCG) to create flexible circuits and high-aspect-ratio electrodes for high-fidelity electromyogram (EMG) recording. The p-NHE enables real-time control of external systems through EMG signals, with deep learning used to optimize electrode placement and achieve high accuracy in classifying finger movements.
Publisher
Nature Communications
Published On
Jul 10, 2020
Authors
Young-Tae Kwon, Yun-Soung Kim, Shinjae Kwon, Musa Mahmood, Hyo-Ryoung Lim, Si-Woo Park, Sung-Oong Kang, Jeongmoon J. Choi, Robert Herbert, Young C. Jang, Yong-Ho Choa, Woon-Hong Yeo
Tags
hybrid electronic systems
human-machine interfaces
biocompatible graphene
electromyogram recording
deep learning
flexible circuits
real-time control

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