构建基于小波熵的自训练半监督支持向量机分类模型评价老年人步态

doi:10.3969/j.issn.0258-8021. 2013. 05.011

Abstract
Figure/Table
References (0)
Related Citation (15)

Download: PDF (571 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract This work investigated the application of the semisupervised learning algorithm to analysis of the unlabeled gait data for evaluating the elderly gait. The novel gait classification algorithm that the selftraining semisupervised support vector machine(SVM) based on wavelet entropy for the discrimination between the young and elderly gait pattern was addressed. In the selftraining, wavelet entropy was employed to obtain labeled samples from unlabeled dataset. The most valuable information related to the gait change was acquired for current gait classifier model, which obviously improved the gait classification performance of SVM. The labeled gait sample datasets including 10 young and 10 elderly participants were used to develop SVM that was employed to classify the unlabelled gait dataset from 120 subjects of different age groups. The new labeled gait data, obtained by our defined wavelet entropy, were selected and constructed the new sample train dataset for developing selftraining SVM. The experimental results showed that the accuracy of our proposed algorithm is 90% in recognization of the young and elderly gait pattern. Furthermore, the accuracy of our proposed algorithm was increased approximately 5% compared with that of the classification algorithm by the supervised support vector machine, suggesting that our proposed technique can obtain more information related to gait change from the labeled and unlabeled gait dataset, and provides a new tool for assessment of elder gait.

Key words： gait analysis semisupervised learning support vector machine wavelet entropy elderly people

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	WU Jian Ning^* WU Bin

Cite this article:

WU Jian Ning^* WU Bin. The SelfTraining SemiSupervised Support Vector Machine Based on Wavelet Entropy for the Evaluation of the Elderly Gait[J]. journal1, 2013, 32(5): 588-594.

URL:

http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021. 2013. 05.011 OR http://cjbme.csbme.org/EN/Y2013/V32/I5/588

［1］中华人民共和国卫生部疾病预防控制局. 老年人跌倒干预技术指南［EB/OL］. http://www.moh.gov.cn, 2011-09-06/2012-11-20.
［2］Giannini S, Catani F, Benedetti MG, et al. Gait analysis: Methodologies and clinical applications ［M］ // Benedetti MG, eds. Terminology, Parameterization and Normalization in Gait Analysis. Amsterdam: IOS Press, 1994:65-88.
［3］Brach JS, Perera S, Studenski S, et al. Meaningful change in measures of gait variability in older adults ［J］. Gait and Posture, 2010, 31(2): 175-179.
［4］Nigg BM, Skleryk BN. Gait characteristics of the elderly ［J］. Clinical Biomechanics, 1988, 3(2):79-87.
［5］Franois P, Hélène C, Réjean H. Gait in the elderly ［J］. Gait and Posture, 1997, 5(2):128-135.
［6］Chau T. A review of analytical techniques for gait data. Part 2: Neural network and wavelet methods ［J］. Gait and Posture, 2001, 13: 102-120.
［7］Wu Jianning, Wang Jue, Liu Li. Feature extraction via KPCA for classification of gait patterns ［J］. Human Movement Science, 2007, 26(3):393-411.
［8］Begg RK, Palaniswami M, Owen B. Support vector machines for automated gait classification ［J］. IEEE Trans on Biomedical Engineering, 2005, 52(9): 828-838.
［9］Khandoker AH, Lai DT, Begg RK, et al. Waveletbased feature extraction for support vector machines for screening balance impairments in the elderly ［J］. IEEE Trans on Neural Systems and Rehabilitation Engineering, 2007, 15(4):587-97.
［10］Begg RK, Kamruzzaman J. A machine learning approach for automated recognition of movement patterns using basic, kinetic and kinematic gait data ［J］. Journal of Biomechanics, 2005, 38: 401-408.
［11］Reginaldo K, Fukuchi B, Bjoern M, et al. Support vector machines for detecting agerelated changes in running kinematics ［J］. Journal of Biomechanics, 2011, 44(3):540-542.
［12］尧德中, 刘铁军, 雷旭, 等. 基于脑电的脑-机接口：关键技术和应用前景［J］. 电子科技大学学报, 2009, 38(5): 550-553.
［13］王金甲，贾敏. 标签均值的自训练半监督支持向量机用于EEG分类［J］. 中国生物医学工程学报, 2011, 30(5):666-672.
［14］Chapelle O, Scholkopf B, Zien A. Semisupervised Learning ［M］ // Joachims T,eds. Transductive Support Vector Machines. Cambridge: MIT Press, 2006:101-112.
［15］吴建宁, 伍滨, 王珏. 用小波熵评价人体步态变化［J］. 航天医学与医学工程, 2009, 22（6）:437-442.
［16］Begg RK, Rahman SM. A method for the reconstruction of ground reaction forcetime characteristics during gait from force platform recordings of simultaneous foot falls ［J］. IEEE Trans on Biomedical Engineering, 2000, 47:547-551.
［17］Chang CC, Lin CJ. LIBSVM: a library for support vector machine ［EB/CP］. http://www.csie.ntu.edu.tw/～cjlin/libsvm, 2009-05-01/2012-11-20.