Reliable estimation of neuromuscular activation is a key enabler for adaptive and personalized control in wearable robotics. However, surface electromyography (EMG) remains difficult to deploy robustly outside laboratory settings due to electrode sensitivity, signal non-stationarity, and strong subject dependence. In this work, we propose an adaptive IMU-to-EMG learning framework that reconstructs continuous muscle activation envelopes from wearable inertial measurements across heterogeneous movement conditions. The approach combines a Transformer encoder with Gaussian Error Gated Linear Units (GEGLU-Transformer) to enhance cross-subject generalization and enable rapid subject-specific personalization. Under a strict leave-one-subject-out (LOSO) protocol on a multi-condition lower-limb biomechanics dataset, the proposed architecture achieves r = 0.706 +/- 0.139 and R^2 = 0.474 +/- 0.208 without subject-specific adaptation. With only 0.5% adaptation data, performance increases to r = 0.761 +/- 0.030 and R^2 = 0.559 +/- 0.047, demonstrating rapid adaptation and early performance saturation. These results support attention-based architectures combined with lightweight adaptation as a practical and scalable alternative to direct EMG sensing for real-world wearable robotic applications.

翻译：可靠地估计神经肌肉激活状态是实现可穿戴机器人自适应与个性化控制的关键。然而，由于电极敏感性、信号非平稳性及强个体依赖性，表面肌电信号（EMG）在实验室环境外仍难以稳健部署。本文提出一种自适应的IMU到EMG学习框架，能从可穿戴惯性测量数据中重构不同运动条件下的连续肌肉激活包络。该方法将Transformer编码器与高斯误差门控线性单元（GEGLU-Transformer）相结合，以增强跨个体泛化能力并实现快速个体特异性个性化。在基于多条件下肢生物力学数据集的严格留一受试者（LOSO）协议下，所提架构在无个体特异性自适应时达到r = 0.706 ± 0.139、R² = 0.474 ± 0.208；仅使用0.5%的自适应数据后，性能提升至r = 0.761 ± 0.030、R² = 0.559 ± 0.047，展现出快速自适应与早期性能饱和特性。这些结果表明，基于注意力的架构结合轻量自适应方法可成为直接EMG传感在实际可穿戴机器人应用中的实用可扩展替代方案。