In sEMG-based recognition systems, accuracy is severely worsened by disturbances, such as electrode shifts by doffing/donning. Traditional recognition models are fixed or static, with limited abilities to work in the presence of the disturbances. In this paper, a transfer learning method is proposed to reduce the impact of electrode shifts. In the proposed method, a novel activation angle is introduced to locate electrodes within a polar coordinate system. An adaptive transformation is utilized to correct electrode-shifted sEMG samples. The transformation is based on estimated shifts relative to the initial position. The experiments acquisition data from ten subjects consist of sEMG signals under eight gestures in seven or nine arbitrary positions, and recorded shifts from a 3D-printed annular ruler. In our extensive experiments, the errors between recorded shifts (as the reference) and estimated shifts is about $-0.017\pm 0.13$ radians. Eight gestures recognition results have shown an average accuracy around 79.32%, which represents a significant improvement over the 35.72% ( $p< 0.0001$ ) average accuracy of results obtained using nonadaptive models, and 60.99% ( $p< 0.0001$ ) results of the other method iGLCM (an improved gray-level co-occurrence matrix). More importantly, by only using one-label samples, the proposed method updates the pre-trained model in an initial position. As a result, the pre-trained model can be adaptively corrected to recognize eight-label gestures in arbitrarily rotary positions. It is proven a highly efficient way to relieve subjects’ re-training burden of sEMG-based rehabilitation systems.

中文翻译：

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电极位移估计和自适应校正以提高基于 sEMG 的识别的鲁棒性

在基于 sEMG 的识别系统中，准确度会因干扰而严重恶化，例如由于落纱/穿衣引起的电极移位。传统的识别模型是固定的或静态的，在存在干扰的情况下工作能力有限。在本文中，提出了一种转移学习方法来减少电极移位的影响。在所提出的方法中，引入了一种新的激活角来定位极坐标系内的电极。自适应变换用于校正电极移位的 sEMG 样本。该转换基于相对于初始位置的估计偏移。来自十个受试者的实验采集数据包括七个或九个任意位置的八个手势下的 sEMG 信号，并记录了来自 3D 打印环形标尺的位移。在我们广泛的实验中，$-0.017\下午 0.13$弧度。八次手势识别结果显示平均准确率约为 79.32%，比 35.72% 有显着提高（ $p<0.0001$ ) 使用非自适应模型获得的结果的平均准确度，以及 60.99% ( $p<0.0001$ ) 其他方法 iGLCM（改进的灰度共生矩阵）的结果。更重要的是，通过仅使用单标签样本，所提出的方法在初始位置更新预训练模型。因此，可以自适应地校正预训练模型以识别任意旋转位置的八标签手势。它被证明是一种高效的方法，可以减轻受试者基于 sEMG 的康复系统的再训练负担。