A New Intelligent Recognition Method for Surface Electromyography in IoT Systems Using OmniXceptionDBN

Surface electromyography (sEMG) is extensively employed to characterize human physiological signals within Internet of Things (IoT) systems, serving as a critical component in Human-Computer Interaction (HCI) and various other applications. Although neural networks have been widely applied to intelligent recognition of sEMG signals, existing methods often face significant challenges in accuracy degradation and computationally intensive processing when handling multisubject signals. To address these issues, this paper proposes a robust surface electromyography (sEMG) intelligent recognition method based on OmniScale XceptionTime-Enhanced Deep Belief Network (OmniXceptionDBN). The method first processes raw signals using Singular Spectrum Analysis (SSA) and Fast Fourier Transform (FFT), then integrates XceptionTime, OmniScaleCNN, and Deep Belief Networks (DBN) to construct the OmniXceptionDBN algorithm for sEMG recognition. The designed integrated network for sEMG signals (i.e., the OmniXceptionDBN algorithm) achieves recognition accuracies of 97.2% for single-subject and 85.9% for multi-subject recognition scenarios without requiring dataset-specific optimizations. Our approach effectively resolves the accuracy degradation when processing across individuals and the high computational complexity inherent in traditional methods, providing an efficient solution for intelligent sEMG recognition.