An important barrier to commercialization of pattern recognition myoelectric control of prostheses is the lack of robustness to confounding factors such as electrode shift, skin impedance variations, and learning effects. To overcome this challenge, a novel supervised adaptation approach based on transfer learning (TL) with convolutional neural networks (CNNs) is proposed which requires only a short training session (a few seconds for each class) to recalibrate the system. TL is proposed as a solution to the problem of insufficient calibration data due to short training times for both classification and regression-based control schemes. This approach was validated for electrode shift of roughly 2.5cm with 13 able-bodied subjects to estimate individual and combined wrist motions. With this method, the original CNN (trained before the shift) was fine-tuned with the calibration data from after shifting. The results show that the proposed technique outperforms training a CNN from scratch (random initialization of weights) or a support vector machine (SVM) using the minimal calibration data. Moreover, it demonstrates superior performance than previous LDA and QDA-based adaptation approaches. As the outcomes confirm, the proposed CNN TL method provides a practical solution for adaptation to external factors, improving the robustness of electromyogram (EMG) pattern recognition systems.

中文翻译：

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一种深度转移学习方法，可减少基于EMG模式识别的控制中电极移位的影响

假体模式识别肌电控制商业化的重要障碍是缺乏对混杂因素（如电极移位，皮肤阻抗变化和学习效果）的鲁棒性。为了克服这一挑战，一种新颖的基于监督的适应方法转移学习（TL） 提出了使用卷积神经网络（CNN）的方法，该方法只需要很短的训练课（每个课程几秒钟）即可重新校准系统。 TL针对分类和基于回归的控制方案，由于训练时间短而提出的校准数据不足的解决方案被提出。该方法已针对13位身体强健的受试者进行约2.5厘米的电极移位进行了验证，以估计其单独和联合的腕部运动。使用这种方法，原始的CNN（在移位之前训练的）已使用移位后的校准数据进行了微调。结果表明，所提出的技术优于使用最少的校准数据从头开始训练CNN（权重的随机初始化）或支持向量机（SVM）。而且，与以前的基于LDA和QDA的自适应方法相比，它具有更出色的性能。结果确认后，拟议的CNNTL 该方法为适应外部因素提供了实用的解决方案，提高了肌电图（EMG）模式识别系统的鲁棒性。