To improve the life quality of forearm amputees, prosthetic hands with high accuracy, and robustness are necessary. The application of surface electromyography (sEMG) signals to control a prosthetic hand is challenging. In this study, we proposed a time-domain CNN model for the regression prediction of joint angles in three degrees of freedom (3-DOFs, include two wrist joint motion and one finger joint motion), and five-fold cross validation was used to evaluate the correlation coefficient (CC). The CC value results of wrist flexion/extension motion obtained from ten participants was 0.87−0.92, pronation/supination motion was 0.72−0.95, and hand grip/open motion was 0.75−0.94. We backtracked the fully connected layer weights to create a geometry plot for analyzing the motion pattern to investigate the learning of the proposed model. In order to discuss the daily updateability of the model by transfer learning, we performed a second experiment on five of the participants in another day and conducted transfer learning based on smaller amount of dataset. The CC results improved (wrist flexion/extension was 0.90−0.97, pronation/supination was 0.84−0.96, hand grip/open was 0.85−0.92), suggesting the effectiveness of the transfer learning by incorporating the small amounts of sEMG data acquired in different days. We compared our CNN-based model with four conventional regression models, the result illustrates that proposed model significantly outperforms the four conventional models with and without transfer learning. The offline result suggests the reliability of the proposed model in real-time control in different days, it can be applied for real-time prosthetic control in the future.

中文翻译：

---

使用回归模型对前臂运动进行多关节角度估计

为了提高前臂截肢者的生活质量，需要高精度、坚固的假手。应用表面肌电图 (sEMG) 信号来控制假手具有挑战性。在本研究中，我们提出了一个时域 CNN 模型，用于三个自由度（3-DOFs，包括两个腕关节运动和一个手指关节运动）的关节角度的回归预测，并使用五折交叉验证来评估相关系数 (CC)。从 10 名参与者获得的手腕屈曲/伸展运动的 CC 值结果为 0.87-0.92，旋前/旋后运动为 0.72-0.95，手握/张开运动为 0.75-0.94。我们回溯了完全连接的层权重以创建几何图，用于分析运动模式以研究所提出模型的学习。为了通过迁移学习讨论模型的每日可更新性，我们在另一天对五名参与者进行了第二次实验，并基于较小的数据集进行了迁移学习。CC 结果得到改善（手腕屈曲/伸展为 0.90-0.97，旋前/旋后为 0.84-0.96，手握/张开为 0.85-0.92），表明通过合并在不同领域获得的少量 sEMG 数据，迁移学习的有效性天。我们将基于 CNN 的模型与四种传统回归模型进行了比较，结果表明，所提出的模型显着优于使用和不使用迁移学习的四种传统模型。离线结果表明所提出模型在不同天数的实时控制中的可靠性，