Dimensionality Reduction and Classification Methods for High-Accuracy EMG Signal Interpretation in Prosthetics

Signals are crucial in conveying information across various fields. This paper presents new methods for processing electromyographic (EMG) signals to create AI systems that decode muscle activity for arm movement control. Using an advanced dataset, the study focuses on enhancing prosthetic control and rehabilitation technologies through sophisticated signal processing and machine learning techniques. Various preprocessing steps improved signal quality, and a diverse set of features was extracted and classified. The results highlight the potential for more intuitive and responsive robotic arm movements, contributing to better prosthetic and rehabilitation solutions.