IEEE Trans Neural Syst Rehabil Eng. 2021;29:777-785. doi: 10.1109/TNSRE.2021.3073751. Epub 2021 Apr 30.
Proper training is essential to achieve reliable pattern recognition (PR) based myoelectric control. The amount of training is commonly determined by experience. The purpose of this study is to provide an offline validation method that makes the offline performance transferable to online control and find the proper amount of training that achieves good online performance. In the offline experiment, eight able-bodied subjects and three amputees participated in a ten-day training. Repeatability index (RI) and classification error (CE) were used to evaluate user learning and machine learning, respectively. The performance of cross-validation (CV) and time serial related validation (TSV) was compared. Learning curves were established with different training trials by TSV. In the online experiment, sixteen able-bodied subjects were randomly divided into two groups with one- or five-trial training, respectively, followed by participating in the test with and without classifier-output feedback. The correlation between offline and online tests was analyzed. Results indicated that five-trial training was proper to train the user and the classifier. The long-term retention of skills could not shorten the learning process. The correlation between CEs of TSV and the online test was strong ( r=0.87 ) with five-trial training, while the correlation between CEs of CV and the online test was weak ( r=0.30 ). Outcomes demonstrate that offline performance evaluated by TSV is transferable to online performance and the learning process can guide the user to achieve good online myoelectric control with minimum training.
适当的训练对于实现可靠的基于模式识别（PR）的肌电控制至关重要。训练的量通常取决于经验。本研究的目的是提供一种离线验证方法，使离线性能能够转移到在线控制，并找到实现良好在线性能的适当训练量。在离线实验中，八名健全受试者和三名截肢者参加了为期十天的训练。重复指数（RI）和分类误差（CE）分别用于评估用户学习和机器学习。比较了交叉验证（CV）和时间序列相关验证（TSV）的性能。通过 TSV 建立了具有不同训练试验的学习曲线。在线实验中，十六名健全受试者随机分为两组，分别进行一或五次训练试验，然后在有或没有分类器输出反馈的情况下参加测试。分析了离线和在线测试之间的相关性。结果表明，五次训练足以训练用户和分类器。技能的长期保留并不能缩短学习过程。五次训练时，TSV 的 CE 与在线测试之间的相关性较强（r=0.87），而 CV 的 CE 与在线测试之间的相关性较弱（r=0.30）。结果表明，TSV 评估的离线性能可以转移到在线性能，并且学习过程可以指导用户以最小的训练量实现良好的在线肌电控制。
