IntroductionSurface Electromyographic (sEMG) signals are widely utilized for estimating finger kinematics continuously in human-machine interfaces (HMI), and deep learning approaches are crucial in constructing the models. At present, most models are extracted on specific subjects and do not have cross-subject generalizability. Considering the erratic nature of sEMG signals, a model trained on a specific subject cannot be directly applied to other subjects. Therefore, in this study, we proposed a cross-subject model based on the Rotary Transformer (RoFormer) to extract features of multiple subjects for continuous estimation kinematics and extend it to new subjects by adversarial transfer learning (ATL) approach.MethodsWe utilized the new subject’s training data and an ATL approach to calibrate the cross-subject model. To improve the performance of the classic transformer network, we compare the impact of different position embeddings on model performance, including learnable absolute position embedding, Sinusoidal absolute position embedding, and Rotary Position Embedding (RoPE), and eventually selected RoPE. We conducted experiments on 10 randomly selected subjects from the NinaproDB2 dataset, using Pearson correlation coefficient (CC), normalized root mean square error (NRMSE), and coefficient of determination (R2) as performance metrics.ResultsThe proposed model was compared with four other models including LSTM, TCN, Transformer, and CNN-Attention. The results demonstrated that both in cross-subject and subject-specific cases the performance of RoFormer was significantly better than the other four models. Additionally, the ATL approach improves the generalization performance of the cross-subject model better than the fine-tuning (FT) transfer learning approach.DiscussionThe findings indicate that the proposed RoFormer-based method with an ATL approach has the potential for practical applications in robot hand control and other HMI settings. The model’s superior performance suggests its suitability for continuous estimation of finger kinematics across different subjects, addressing the limitations of subject-specific models.

中文翻译：

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用于连续估计指关节运动学的旋转变压器跨学科模型和新学科的迁移学习方法

简介表面肌电 (sEMG) 信号广泛用于人机界面 (HMI) 中连续估计手指运动学，深度学习方法对于构建模型至关重要。目前，大多数模型都是针对特定主题提取的，不具有跨主题的泛化性。考虑到表面肌电信号的不稳定性质，针对特定受试者训练的模型不能直接应用于其他受试者。因此，在本研究中，我们提出了一种基于旋转变压器（RoFormer）的跨主题模型，提取多个主题的特征以进行连续估计运动学，并通过对抗性迁移学习（ATL）方法将其扩展到新主题。受试者的训练数据和 ATL 方法来校准跨受试者模型。为了提高经典Transformer网络的性能，我们比较了不同位置嵌入对模型性能的影响，包括可学习绝对位置嵌入、正弦绝对位置嵌入和旋转位置嵌入（RoPE），最终选择了RoPE。我们对 NinaproDB2 数据集中随机选择的 10 个受试者进行了实验，使用 Pearson 相关系数 (CC)、归一化均方根误差 (NRMSE) 和确定系数 (R2) 作为性能指标。结果将所提出的模型与其他四个模型进行了比较包括 LSTM、TCN、Transformer 和 CNN-Attention。结果表明，无论是在跨学科还是特定学科的情况下，RoFormer 的性能均明显优于其他四个模型。此外，ATL 方法比微调 (FT) 迁移学习方法更好地提高了跨主题模型的泛化性能。讨论结果表明，所提出的基于 RoFormer 的 ATL 方法具有在机器人中实际应用的潜力手动控制和其他 HMI 设置。该模型的卓越性能表明它适合连续估计不同受试者的手指运动学，解决了特定受试者模型的局限性。