Sequential Learning on sEMGs in Short- and Long-term Situations via Self-training Semi-supervised Support Vector Machine

The purpose of this study it to assess the effect of sequential learning of self-training support vector machine (ST-S3VM) on short- and long-term surface electromyogram (sEMG) datasets. A machine learning-based supervised classi-fier is enabling stable, complex, and high-performance motion control. Unlabeled sEMG measurements are easy by the devel-opment of wearable sensing technology. Thus, semi-supervised learning methods are attracted attention to utilize unlabeled sEMG data for supervised classifier with a small amount of labeled data. To evaluate robustness of ST-S3VM in realistic conditions, two public datasets which respectively contain a short- and long-term dataset were used. We compared the performance of ST-S3VM with four-kinds of SVM classifiers. In both short- and long-term situations, ST combined classifiers (ST-SVM and ST-S3VM) showed higher performances than the methods without ST (SVM and S3VM). In some cases, ST-S3VM had the best performance, but in other cases, ST-SVM had better performance than ST-S3VM. In order to make better use of unlabeled data, we will develop ST-S3VM to reduce the impact of harmful unlabeled data.