Research of Fast Transfer Learning of sEMG Based on MPCNN Model for Gesture Recognition Applications

To address the challenge of inter-individual variability and improve the universality of gesture recognition technology, this study proposes a migration learning strategy based on Multi Parallel Conventional Neural Network (MPCNN), which aims to achieve efficient gesture recognition based on surface Electromyogram (sEMG) signals through a parallel architecture and an optimized migration learning mechanism. With a parallel architecture and optimized migration learning mechanism, MPCNN can deal with physiological differences between individuals more efficiently than previous CNN migration frameworks, which improves the model's adaptability to new users and recognition accuracy. In addition, MPCNN significantly enhances the utility of the system by reducing the model training time and improving the generalization ability. Through multiple sets of experiments, including multiplicative cross-validation, ablation experiments, and robustness tests, this study validates the effectiveness of the proposed strategy in several respects. The experimental results demonstrate that MPCNN significantly improves the accuracy of gesture recognition compared to traditional CNN models, and the proposed MPCNN migration learning strategy achieves a recognition rate of 94.95% in Ninapro DB7 compared to previous CNN migration learning frameworks, with an improvement of 4.38 percentage points, with the training time reduced by more than 50%. These experiments validate the advantages of the MPCNN migration model in reducing the training burden, enhancing the generalization ability, and improving anti-interference. The human-computer interaction capability is validated based on an experimental model, which verifies its promising potential for myoelectric control applications.