In the field of prosthetics, researchers have recognized the potential of utilizing the remaining muscles in the residual limb, which can contract and provide valuable information for predicting human intentions. Electromyography (EMG) is currently the most widely adopted technique for extracting such information, which can then be employed to control prosthetic devices. While Machine Learning (ML) algorithms are frequently utilized for EMG signal analysis (e.g., diagnostics), their application in the realm of prosthetics is largely limited to laboratory settings. This limitation arises from the dynamic nature of EMG signals, which continuously evolve over time due to many factors such as muscle fatigue, sensor position shifts, and sweating [1, 2].