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This paper introduces a graph neural network model for real-time hand gesture recognition using surface electromyography (sEMG) signals from the forearm. The method achieves 99% classification accuracy with an average processing time of 48ms on an M1 Pro CPU, outperforming existing state-of-the-art techniques.