Electromyography (EMG) electrodes are critical for detecting and interpreting muscle activity, which is essential for operating prosthetic devices and wearable robots. Traditional EMG electrodes, however, often face limitations such as being uncomfortable, lacking stretchability, and wearing out quickly. To overcome these challenges, we developed an innovative EMG wristband with mesh electrodes created using charge-reverse electro writing (CREW). The wristband is tailored to fit the unique muscle distribution of the user, featuring a special fiber with a core/shell structure. The core, enriched with liquid metal nanoparticles (LM-NPs), ensures excellent electrical conductivity, while the thermoplastic polyurethane (TPU) shell enhances flexibility, durability, and washability. The wristband is also designed for long-term comfort, with a breathable 3D scaffold structure that allows natural skin ventilation. Even under maximum strain, it maintains high signal clarity, achieving a signal-to-noise ratio (SNR) of over 30 decibels. The signals are processed through a machine learning algorithm, the multilayer perceptron (MLP), with minimal delay time, enabling smooth and human-like motor movements in prosthetic devices. This breakthrough addresses key challenges in traditional electrodes, providing a reliable, high-performance solution for wearable robotics and assistive technologies, with a focus on comfort, durability, and seamless integration into everyday life.