Abstract
Upper limb amputation can severely restrict the mobility and ability of amputees to perform daily activities. In addressing this issue, deep learning algorithms and electromyography pattern recognition have emerged as promising clinical solutions for functional upper-limb prosthetics. This article presents the use of EMG sensors to capture muscle movement signals and applies the pattern recognition function of a 1D convolutional neural network to identify these signals and control the movement of prosthetics. Experimental results demonstrate that the convolutional neural network exhibits fast training and high-precision recognition capabilities enabling it to accurately identify muscle signals and effectively control prosthetic movements.