An Integrated Soft Computing Framework for EMG Signal Processing and Muscle Movement Classification

Electromyography (EMG) signals are critical for decoding neuromuscular intent in neural interfaces, prosthetics, rehabilitation robotics, and wearable diagnostics. Challenges including nonlinearity, nonstationarity, noise, and inter-subject variability undermine traditional processing methods in dynamic real-world scenarios. This study reviews these issues and proposes a hybrid soft-computing architecture that fuses preprocessing, multi-domain feature extraction, machine learning (ML), deep learning (DL), and fuzzy logic for superior muscle-activity classification. ML delivers efficient classifiers; DL captures spatiotemporal dynamics from raw signals; fuzzy logic handles activation uncertainties amid noise. Modular and scalable, the framework adapts across datasets for prosthetic control, rehab evaluation, and neuromuscular monitoring. Methodological details and conceptual benchmarks highlight its advantages, unifying legacy techniques with AI to enable validation studies and real-time neural prosthetics.