Surface electromyogram (sEMG) serves as a means to discern human movement intentions, achieved by applying epidermal electrodes to specific body regions. However, it is difficult to obtain high-fidelity sEMG recordings in areas with intricate curved surfaces, such as the body, because regular sEMG electrodes have stiff structures. In this study, we developed myoelectrically sensitive hydrogels via 3D printing and integrated them into a stretchable, flexible, and high-density sEMG electrodes array. This electrode array offered a series of excellent human-machine interface (HMI) features, including conformal adherence to the skin, high electron-to-ion conductivity (and thus lower contact impedance), and sustained stability over extended periods. These attributes render our electrodes more conducive than commercial electrodes for long-term wearing and high-fidelity sEMG recording at complicated skin interfaces. Systematic in vivo studies were used to investigate its efficacy to control a prosthetic hand by decoding sEMG signals from the human hand via a multiple-channel readout circuit and a sophisticated artificial intelligence algorithm. Our findings demonstrate that the 3D printed gel myoelectric sensing system enables real-time and highly precise control of a prosthetic hand.
© 2025. The Author(s).