Dual-Stream SPP-CNN for High-Precision sEMG Gesture Recognition in Human-Machine Interfaces

Surface electromyography (sEMG) signals directly reflect movement intention and are therefore promising for natural human-machine interaction. However, their inherent non-stationarity and high inter-subject variability remain major obstacles to robust feature extraction and model generalization. To address these challenges, this study proposes a dual-stream spatial pyramid pooling convolutional neural network (DSSCNN). In this framework, one-dimensional sEMG segments are transformed into two complementary image representations, continuous wavelet transform (CWT) spectrograms and Gramian angular difference field (GADF) images, forming a dual-channel input that jointly preserves time-frequency dynamics and temporal correlation structures. A dual-stream convolutional architecture then extracts discriminative features from each modality, after which a spatial pyramid pooling (SPP) layer aggregates multi-scale representations, enhancing the network's capacity to capture robust spatiotemporal patterns. Extensive experiments demonstrate that DSSCNN achieves an average gesture recognition accuracy of 97.88% with low inter-subject variance under intra-subject random split, and 96.59% under leave-one-subject-out (LOSO) protocol. The practical viability of the proposed approach is further validated through real-time control of an unmanned ground vehicle (UGV). These results not only indicate that the dual-stream framework combined with SPP layer provides an effective strategy for high-precision sEMG-based gesture recognition but also provides a promising technical pathway toward next-generation natural human-machine interaction.