Organohydrogel-Based Soft SEMG Electrodes for Algorithm-Assisted Gesture Recognition

Abstract

Epidermal electronics that can monitor physiological signals such as surface electromyogram (sEMG) signals attract widespread attentions in personalized healthcare, human–machine interfaces (HMI) and virtual/augmented reality (AR/VR). However, conventional electromyographic electrodes suffer from skin discomfort, susceptibility to motion artifact interference, and short service lifetime. Here, an organohydrogel-based sEMG electrode endows with high conductivity, low modulus and long-term stability is developed by doping partially reduced graphene oxide (pRGO) into highly cross-linked organohydrogel network. The as-fabricated polyacrylamide/sodium alginate/tannic acid/partially reduced graphene oxide (PAM/SA/TA/pRGO) organohydrogel possesses farewell conductivity (4.22 S m⁻¹) while preserving tissue-like compliance (Young's modulus ≈32 KPa), excellent stretchability (≈600%), high adhesion as well as superior anti-drying properties. In addition, a stretchable sEMG electrode for long-term reliable service is fabricated via immobilizing the organohydrogel electrodes onto a flexible very high bond (VHB) substrate. As a result, the integrated electrodes show high signal-to-noise ratio (SNR) (35.15 db) comparable to that of the commercial electrodes. Furthermore, with assistance of deep learning, the proposed sEMG electrodes obtain high identification accuracy of 97.11% in distinguishing sophisticated gestures. This system can be further exploited for real-time tele-operations and offers broad prospects in human–machine immersive interactive application.