Electromyography (EMG)-based gesture recognition has emerged as a promising approach for human-computer interaction. However, its performance is often limited by the scarcity of labeled EMG data, significant cross-user variability, and poor generalization to unseen gestures. To address these challenges, we propose SeqEMG-GAN, a conditional, sequence-driven generative framework that synthesizes high-fidelity EMG signals from hand joint angle sequences. Our method introduces a context-aware architecture composed of an angle encoder, a dual-layer context encoder featuring the novel Ang2Gist unit, a deep convolutional EMG generator, and a discriminator, all jointly optimized via adversarial learning. By conditioning on joint kinematic trajectories, SeqEMG-GAN is capable of generating semantically consistent EMG sequences, even for previously unseen gestures, thereby enhancing data diversity and physiological plausibility. Experimental results show that classifiers trained solely on synthetic data experience only a slight accuracy drop (from 57.77% to 55.71%). In contrast, training with a combination of real and synthetic data significantly improves accuracy to 60.53%, outperforming real-only training by 2.76%. These findings demonstrate the effectiveness of our framework,also achieves the state-of-art performance in augmenting EMG datasets and enhancing gesture recognition performance for applications such as neural robotic hand control, AI/AR glasses, and gesture-based virtual gaming systems.

翻译：基于肌电图（EMG）的手势识别已成为人机交互领域一种前景广阔的技术路径。然而，其性能常受限于标记肌电数据的稀缺性、显著的跨用户差异性以及对未见过手势的泛化能力不足。为应对这些挑战，我们提出SeqEMG-GAN——一种基于手部关节角度序列合成高保真肌电信号的条件式序列驱动生成框架。本方法引入了一种上下文感知架构，该架构由角度编码器、包含新型Ang2Gist单元的双层上下文编码器、深度卷积肌电生成器及判别器构成，并通过对抗学习进行联合优化。通过以关节运动轨迹为条件，SeqEMG-GAN能够生成语义一致的肌电序列，即使对于先前未见过的手势亦能实现，从而显著提升数据多样性与生理合理性。实验结果表明，仅使用合成数据训练的分类器仅出现轻微精度下降（从57.77%降至55.71%）。相比之下，结合真实与合成数据进行训练可将精度显著提升至60.53%，较纯真实数据训练提升2.76%。这些发现验证了本框架在增强肌电数据集及提升手势识别性能方面的有效性，在神经义肢控制、AI/AR眼镜及基于手势的虚拟游戏系统等应用中达到了当前最优性能水平。