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Hand amputation is a highly debilitating event that has severe physical and psychological impacts on an individual's life. While efforts have been made to restore lost functionality through dexterous myoelectric prosthetic hands, achieving natural and robust control for daily use remains challenging. This paper presents a novel biomimetic robotic hand design that aims to provide an affordable, high-precision, and highly applicable solution to better assist those with hand disabilities in everyday living. The proposed design is based on a dual-linkage structure, where each finger is driven by two servomotors to mimic the natural motion of the human hand. The design is based on a double linkage structure, each finger is driven by two servo motors, and supports flexible resistors to detect muscle movements. It adopts a Raspberry Pi control system with a built-in TGAM brainwave monitoring module, which can finely control the movement of the manipulator through brainwaves. The robot has more than ten common gestures built-in, including grasping, stretching, turning, etc. It is made of 3D printing and flexible silicone, with an ergonomic shape. It is powered by a lithium battery and supports USB 3.0 charging. The design realizes accurate motion control and real-time assisted grip adjustment through stress analysis, deep learning and other technologies. The linkage structure improves the control precision, the brain wave control enhances the human-machine interaction, and the FPV real-time video transmission technology and TensorFlow deep learning framework realize the intelligent grip adjustment to meet the daily life needs of the disabled.
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