吉林大学学报(工学版) ›› 2013, Vol. 43 ›› Issue (05): 1415-1426.doi: 10.7964/jdxbgxb201305042

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

LI Yang1, TIAN Yan-tao2,3, CHEN Wan-zhong2   

  1. 1. Information Engineering College, Beijing Institute of Petrochemical Technology, Beijing 102617, China;
    2. College of Communication Engineering, Jilin University, Changchun 130022, China;
    3. Key Laboratory of Bionic Engineering, Ministry of Education, Jilin University, Changchun 130022, China
  • Received:2012-05-14 Online:2013-09-01 Published:2013-09-01

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Abstract:

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 of human labeled samples, high classification results, which has practical significance in sEMG pattern recognition

Key words: information processing technology, sEMG, semi-supervised algorithm, boosting, multi-class classification

CLC Number: 

  • TN911.7

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