Vidovic Marina M-C, Paredes Liliana P, Amsüss Sebastian, Pahl Jaspar, Hahne Janne M, Graimann Bernhard, Farina Dario, Müller Klaus-Robert
Annu Int Conf IEEE Eng Med Biol Soc. 2014;2014:4370-3. doi: 10.1109/EMBC.2014.6944592.
Ensuring robustness of myocontrol algorithms for prosthetic devices is an important challenge. Robustness needs to be maintained under nonstationarities, e.g. due to electrode shifts after donning and doffing, sweating, additional weight or varying arm positions. Such nonstationary behavior changes the signal distributions - a scenario often referred to as covariate shift. This circumstance causes a significant decrease in classification accuracy in daily life applications. Re-training is possible but it is time consuming since it requires a large number of trials. In this paper, we propose to adapt the EMG classifier by a small calibration set only, which is able to capture the relevant aspects of the nonstationarities, but requires re-training data of only very short duration. We tested this strategy on signals acquired across 5 days in able-bodied individuals. The results showed that an estimator that shrinks the training model parameters towards the calibration set parameters significantly increased the classifier performance across different testing days. Even when using only one trial per class as re-training data for each day, the classification accuracy remained > 92% over five days. These results indicate that the proposed methodology can be a practical means for improving robustness in pattern recognition methods for myocontrol.
确保用于假肢装置的肌电控制算法的稳健性是一项重要挑战。在非平稳情况下需要保持稳健性，例如由于穿戴和脱下后电极移位、出汗、额外负重或手臂位置变化等原因。这种非平稳行为会改变信号分布，这种情况通常被称为协变量偏移。这种情况会导致日常生活应用中的分类准确率显著下降。重新训练是可行的，但很耗时，因为它需要大量试验。在本文中，我们建议仅通过一个小的校准集来调整肌电分类器，该校准集能够捕捉非平稳性的相关方面，但只需要非常短持续时间的重新训练数据。我们在健全个体中对连续5天采集的信号测试了这种策略。结果表明，一种将训练模型参数向校准集参数收缩的估计器显著提高了不同测试日的分类器性能。即使每天仅使用每个类别的一次试验作为重新训练数据，五天内分类准确率仍保持>92%。这些结果表明，所提出的方法可以成为提高肌电控制模式识别方法稳健性的一种实用手段。
