DiffSpkSync: A Muscle Synergy-Guided Spiking Diffusion Model for EMG Signal Generation to Improve Gesture Recognition Performance

High-density surface electromyography (HD-sEMG) based hand gesture recognition (HGR) has shown great promise for intuitive human-machine interaction. However, the performance of HGR model is often hindered by a scarcity of available training data, especially in the fields of gesture recognition, rehabilitation, and medicine. To address these issues, we propose DiffSpkSync, a novel generative framework that integrates (1) muscle synergy-guided diffusion modeling for physiologically plausible signal reconstruction, (2) spiking neuron-based sparsification to reduce energy cost, and (3) a time-series mixup strategy to preserve local dynamics during augmentation. Experiments on a public Hyser dataset and a self-collected XDHDEMG dataset demonstrate that training gesture classifiers with data augmented by DiffSpkSync consistently improves classification accuracy in both intrasession and intersession scenarios. Comparative results further demonstrate superior performance over representative generative baselines, including VAE, DCGAN, DANN-CRC, and PatchEMG. Furthermore, real-time validation demonstrates that the proposed method achieves an average of 130.22 ms end-to-end latency and an average of 95.87% accuracy predictions, supporting their applicability in real-world applications.