Background: The development of dexterous and intuitive prosthetic hands remains a significant challenge in rehabilitation engineering. Myoelectric control systems, which interpret surface electromyography (sEMG) signals from residual muscles, offer a promising avenue for non-invasive human-machine interfacing. However, traditional systems often suffer from limited accuracy, slow response times, and a lack of robustness to real-world conditions, hindering their clinical viability and user adoption. This article provides a comprehensive review of recent advancements aimed at overcoming these limitations.

Methods: We conducted a systematic review of contemporary literature focused on sEMG-based prosthetic control. The analysis covers the full spectrum of the control pipeline, including signal acquisition, pre-processing, and, most critically, feature extraction and pattern recognition. A special emphasis is placed on the transition from traditional machine learning classifiers to advanced deep learning architectures, particularly Convolutional Neural Networks (CNNs), for decoding hand gestures. The performance of these models is evaluated based on key metrics such as classification accuracy, computational latency, and robustness.

Results: The synthesis of recent findings reveals a clear trend: deep learning models, especially CNNs, consistently outperform traditional methods in hand gesture recognition accuracy, often exceeding 95% in controlled settings. Studies demonstrate that CNNs can automatically learn discriminative features from raw or minimally processed sEMG signals, eliminating the need for complex manual feature engineering. Furthermore, hybrid models and optimized network architectures have shown significant progress in achieving the low latency required for real-time prosthetic control.

Conclusion: Advanced signal analysis, powered by deep learning, represents a paradigm shift in myoelectric prosthetic control. These techniques are paving the way for more natural, reliable, and dexterous artificial limbs. Despite this progress, challenges related to inter-user variability, long-term stability, and clinical translation remain. Future research should focus on developing more generalizable models, integrating sensory feedback, and conducting extensive real-world usability studies to bridge the gap between laboratory breakthroughs and practical application.

Prosthetic Control, Surface Electromyography (sEMG), Hand Gesture Recognition, Machine Learning

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