Abstract: To solve the problem of inter-individual variability and improve the universality of gesture recognition technology, this paper proposes a migration learning strategy based on Multi Parallel Conventional Neural Network (MPCNN), which aims to achieve efficient gesture recognition based on surface electromyography through a parallel architecture and an optimized migration learning mechanism. This strategy aims to achieve efficient gesture recognition based on surface EMG signals. 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 confirms the effectiveness of the proposed strategy in several aspects. The experimental results show that MPCNN significantly improves the accuracy of gesture recognition compared to traditional CNN models, and the MPCNN migration learning strategy proposed in this paper achieves a recognition rate of 94.95% in Ninapro DB7 compared to previous CNN migration learning frameworks with an improvement of 4.38%, while the training time is 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 the anti-interference. The human-computer interaction capability is validated based on the experimental model, which verifies its promise for myoelectric control applications.

摘要： 为解决个体间差异性的问题并提高手势识别技术的普适性，本文提出了一种基于多并行卷积网络（Multi Parallel Conventional Neural Network, MPCNN）的迁移学习策略。该策略旨在实现基于表面肌电信号的高效手势识别。MPCNN通过并行架构和优化的迁移学习机制，对比以往的CNN迁移框架能够更有效地处理不同个体间的生理差异，从而提高了模型对新用户的适应性和识别准确率。此外，MPCNN通过减少模型训练时间和提高泛化能力，显著增强了系统的实用性。通过多组实验，包括倍数交叉验证、消融实验和健壮性测试，本研究证实了所提策略在多个方面的有效性。实验结果显示，与传统CNN模型相比，MPCNN在手势识别准确率方面提升显著，本文提出的MPCNN迁移学习策略在Ninapro DB7识别率达到了94.95%对比以往的CNN迁移学习框架有4.38%的提升，同时训练时间减少了超过50%。这些实验验证了MPCNN迁移模型在减轻训练负担、增强泛化能力和提高抗干扰性方面的优点。基于实验模型对人机交互能力进行了验证，验证了其在肌电控制应用前景。