Myoelectric signals are electrical impulses generated during muscle contraction and are primarily used in prosthetics and rehabilitation. By detecting and interpreting these signals, prosthetic devices can be precisely controlled by individuals with limb loss or limb impairment. This paper explores the expansion of myoelectric signal (MES) applications beyond traditional prosthetic control by integrating advanced signal processing techniques. Leveraging three decades of research and development in foot- and hand-prosthetic control, coupled with the rise of artificial intelligence (AI) and increased computing power, the authors see a growing demand in computer games, interactive simulations, robotics, and the medical sector. A novel approach uses AI-based classification for pre-processed MES, inspired by fuzzy audio search algorithms and convolutional neural networks (CNNs).