A myoelectric prosthetic hand is controlled by a pipeline many developers already know: sense, clean, extract features, classify, act. The difference is that the sensor is a person’s forearm. This talk walks through that pipeline using electromyography (EMG), the electrical signal muscles produce, and shows why prosthetic control is a real-time machine learning problem as well as a hardware one. We cover what works in the lab, what breaks in daily use, and how to build your own EMG project over a weekend. What you’ll learn: The five stages of a prosthetic control pipeline, and how each maps to concepts developers already know How raw EMG is filtered, windowed and reduced to features a classifier can use Why pattern recognition and latency decide whether a prosthesis feels natural How to start your own EMG project, with DIY resources and open datasets