This paper explores non-invasive use of surface electromyographic (sEMG) signals from a human arm for controlling various devices. In recent years, numerous studies have explored sEMG-based gesture recognition for prosthetic and robotic applications. Gesture sets are often limited to a small number of movements, restricting the range of control. Furthermore, the translation of offline classification results to real-time robotic control remains challenging due to latency, signal variability, and