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

• paper • Previous Articles     Next Articles

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

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

[1] 刘利, 刘萍萍, 韦佳. 用于带边信息人脸数据的半监督维数约减算法[J]. 吉林大学学报:工学版, 2011, 41(1): 189-193. Liu Li, Liu Ping-ping, Wei Jia. Semi-supervised dimensionality reduction algorithm applying in face data with side information[J]. Journal of Jilin Universit (Engineering and Technology Edition), 2011, 41(1): 189-193.

[2] 董元方, 李雄飞, 李军, 等. XML文档分类的IL-Adaboost算法[J]. 吉林大学学报:工学版, 2011, 41(4): 1054-1058. Dong Yuan-fang, Li Xiong-fei, Li Jun, et al. IL-adaboost algorithm for XML document classification[J]. Journal of Jilin University(Engineering and Technology Edition), 2011, 41(4): 1054-1058.

[3] Bennett K P, Demiriz A. Semi-supervised support vector machine[C]//NIPS, Denver, USA: IEEE, 1999:368-374.

[4] Chapelle O, Zien A. Semi-supervised classification by low fensity deperation[C]//10th Int Workshop on AI and Stat, USA: IEEE, 2005:57-64.

[5] Blum A, Chawla S. Learning from labeled and unlabeled data using graph mincuts[C]//ICML CA, USA: IEEE, 2001:19-26.

[6] Zhu X, Ghahramani Z, Lafferty J. Semi-supervised learning using Gaussian fields and Harmonic functions[C]//ICML, Washington D C, USA: IEEE, 2003: 58-65.

[7] Zhou D, Bousquet O, Lal T, et al. Scholkopf B learning with local and global vonsistency[C]//COLT Cambridge, MA: MIT Press, 2004:321-328.

[8] Belkin M, Matveeva I, Niyogi P. Regularization and semisupervised learning on large graphs[C]//COLT,2004: 624-638.

[9] Bennett K P, Demiriz A, Maclin R. Exploiting unlabeled fata in ensemble methods[C]//KDD,Edmonton, Canada. 2002:289-296.

[10] Chen K, Wang S. Regularized boost for semi-supervised learning[C]//NIPS,2008:281-288.

[11] Jin R, Zhang J. Multi-class learning by smoothed boosting[J]. Mach Learn,2007, 67(3): 207-227.

[12] Scholkopf B, Smola A J. Learning with kernels: support vector machines, regularization, optimization, and Beyond[P]. MIT Press, Cambridge, MA,2002.

[13] Zadrozny B, Elkan C. Transforming classifier scores into accurate multiclass probability estimates[C]//KDD,New York, USA: ACM Press, 2002:694-699.

[14] Dalche Buc F, Grandvalet Y, Ambroise C. Semi-supervised margin boost//NIPS, Cambridge, MA: MIT Press, 2002:553-560.

[15] Higham N J. Matrix nearness problems and applications: Applications of matrix theory[P].Oxford University Press,1989: 1-27.

[16] 李阳, 田彦涛, 陈万忠. 基于FFT盲辨识的肌电信号建模及模式识别[J]. 自动化学报, 2011, 38(1):128-134. Li Yang, Tian Yan-tao, Chen Wan-zhong. Modeling and classifying of sEMG based on FFT blind identification[J]. Acta Automatica Sinica, 2011, 38(1):128-134.

[17] Leo G. Gradient boosting trees for auto insurance loss cost modeling and prediction[J].Expert Systems with Applications, 2012, 39: 3659-3667.

[1] YING Huan,LIU Song-hua,TANG Bo-wen,HAN Li-fang,ZHOU Liang. Efficient deterministic replay technique based on adaptive release strategy [J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(6): 1917-1924.
[2] LIU Zhong-min,WANG Yang,LI Zhan-ming,HU Wen-jin. Image segmentation algorithm based on SLIC and fast nearest neighbor region merging [J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(6): 1931-1937.
[3] SHAN Ze-biao,LIU Xiao-song,SHI Hong-wei,WANG Chun-yang,SHI Yao-wu. DOA tracking algorithm using dynamic compressed sensing [J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(6): 1938-1944.
[4] YAO Hai-yang, WANG Hai-yan, ZHANG Zhi-chen, SHEN Xiao-hong. Reverse-joint signal detection model with double Duffing oscillator [J]. 吉林大学学报(工学版), 2018, 48(4): 1282-1290.
[5] QUAN Wei, HAO Xiao-ming, SUN Ya-dong, BAI Bao-hua, WANG Yu-ting. Development of individual objective lens for head-mounted projective display based on optical system of actual human eye [J]. 吉林大学学报(工学版), 2018, 48(4): 1291-1297.
[6] CHEN Mian-shu, SU Yue, SANG Ai-jun, LI Pei-peng. Image classification methods based on space vector model [J]. 吉林大学学报(工学版), 2018, 48(3): 943-951.
[7] CHEN Tao, CUI Yue-han, GUO Li-min. Improved algorithm of multiple signal classification for single snapshot [J]. 吉林大学学报(工学版), 2018, 48(3): 952-956.
[8] MENG Guang-wei, LI Rong-jia, WANG Xin, ZHOU Li-ming, GU Shuai. Analysis of intensity factors of interface crack in piezoelectric bimaterials [J]. 吉林大学学报(工学版), 2018, 48(2): 500-506.
[9] LIN Jin-hua, WANG Yan-jie, SUN Hong-hai. Improved feature-adaptive subdivision for Catmull-Clark surface model [J]. 吉林大学学报(工学版), 2018, 48(2): 625-632.
[10] WANG Ke, LIU Fu, KANG Bing, HUO Tong-tong, ZHOU Qiu-zhan. Bionic hypocenter localization method inspired by sand scorpion in locating preys [J]. 吉林大学学报(工学版), 2018, 48(2): 633-639.
[11] YU Hua-nan, DU Yao, GUO Shu-xu. High-precision synchronous phasor measurement based on compressed sensing [J]. 吉林大学学报(工学版), 2018, 48(1): 312-318.
[12] WANG Fang-shi, WANG Jian, LI Bing, WANG Bo. Deep attribute learning based traffic sign detection [J]. 吉林大学学报(工学版), 2018, 48(1): 319-329.
[13] LIU Dong-liang, WANG Qiu-shuang. Instantaneous velocity extraction method on NGSLM data [J]. 吉林大学学报(工学版), 2018, 48(1): 330-335.
[14] TANG Kun, SHI Rong-hua. Detection of wireless sensor network failure area based on butterfly effect signal [J]. 吉林大学学报(工学版), 2017, 47(6): 1939-1948.
[15] LI Juan, MENG Ke-xin, LI Yue, LIU Hui-li. Seismic signal noise suppression based on similarity matched Wiener filtering [J]. 吉林大学学报(工学版), 2017, 47(6): 1964-1968.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!