基于半监督boosting表面肌电信号多类模式识别

sEMG multi-class pattern recognition based on semi-supervised boosting algorithm

针对表面肌电信号较为复杂,且获取标注样本代价较大的问题,提出了基于半监督boosting学习的表面肌电信号多类模式识别方法。与目前半监督boosting算法着重解决两类分类问题,将多类问题转化为多个两类问题不同,本文方法通过联合分类置信度及样本间相似度确定每次迭代过程中未标注样本的预测类别,达到利用未标注样本提高多类问题正确识别率的目的,避免了将某一样本划分多类的问题。由实验分析可知,本文算法与现有半监督boosting算法相比,正确识别率更高,对于不同标注样本数及不同基分类器具有较好的鲁棒性。本文方法降低了人工标注代价,对多类问题具有良好的识别效果。

The surface electromyographic signal （sEMG） is often complicated, and it is expensive and time-consuming to obtain labeled samples of sEMG, especially when it has to be done manually by experts. To overcome such problems, a new semi-supervised boosting algorithm is used to classify multi-class problem of sEMG in this paper. This algorithm can use a large number of unlabeled samples together with a small number of labeled samples to build a better learner. Most semi- supervised algorithms have been designed for binary classification. The shortcoming is that when extended to multi-class classification these algorithms are unable to exploit the fact that each sample is only assigned to one class. The advantage of the new semi-supervised boosting algorithm used in this paper is that it exploits both classification confidence and similarities among samples when deciding the pseudo-labels for unlabeled samples. Empirical study with six movements of sEMG show＇ that the new algorithm performs better than the state-of-the-art boosting algorithms for semi-supervised learning.It gives large reduction in the number o{ human labeled samples, high classification results, which has practical significance in sEMG pattern recognition