Myoelectric Signals (MES) are electrical impulses produced by muscles during contraction and play a critical role in prosthetics and rehabilitation. These signals are generated naturally by the body and can be captured by sensors, allowing precise control of prosthetic end-effectors. By interpreting the patterns of these electrical impulses, individuals with limb loss or mobility impairments can effectively operate advanced prosthetic devices. This technology enables users to regain a higher level of autonomy as the prosthetic ideally responds to the user’s intentions in near real-time, providing a seamless and intuitive experience in daily activities. In this contribution, we present a method that takes advantage of both the Artificial Intelligence (AI) embedded in the prosthetic device itself and, when available, cloud-based AI classification. This dual approach allows for more sophisticated decision making and control.