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题名: Missing-Data Classification with the Extended Full-Dimensional Gaussian Mixture Model: Applications to EMG-Based Motion Recognition
作者: Ding QC(丁其川); Han JD(韩建达); Zhao XG(赵新刚); Chen Y(陈洋)
作者部门: 机器人学研究室
关键词: Classification ; electromyography (EMG) ; Gaussian mixture model (GMM) ; missing data ; myoelectric hand
刊名: IEEE Transactions on Industrial Electronics
ISSN号: 0278-0046
出版日期: 2015
卷号: 62, 期号:8, 页码:4994-5005
收录类别: SCI ; EI
产权排序: 1
摘要: Missing data are a common drawback that pattern recognition techniques need to handle when solving real-life classification tasks. This paper first discusses problems in handling high-dimensional samples with missing values by the Gaussian mixture model (GMM). Since fitting the GMM by directly using high-dimensional samples as inputs is difficult due to the convergence and stability issues, a novel method is proposed to build the high-dimensional GMM by extending a reduced-dimensional GMM to the full-dimensional space. Based on the extended full-dimensional GMM, two approaches, namely, marginalization and conditional-mean imputation, are proposed to classify samples with missing data in online phase. Then, the proposed methods were employed to recognize hand motions from surface electromyography (sEMG) signals, and more than 75% of classification accuracy of motions can be obtained even if 50% of sEMG signals were missing. Comparisons with normal mean and zero imputations also demonstrate the improvements of the proposed methods. Finally, a control scheme for a myoelectric hand was designed by involving the novel methods, and online experiments confirm the ability of the proposed methods to improve the safety and stability of practical systems.
语种: 英语
WOS记录号: WOS:000357268300033
WOS标题词: Science & Technology ; Technology
类目[WOS]: Automation & Control Systems ; Engineering, Electrical & Electronic ; Instruments & Instrumentation
关键词[WOS]: SUPPORT VECTOR MACHINES ; MYOELECTRIC CONTROL ; FAULT-DIAGNOSIS ; IMPUTATION ; VALUES ; ANALYZERS ; SYSTEM ; SIGNAL
研究领域[WOS]: Automation & Control Systems ; Engineering ; Instruments & Instrumentation
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内容类型: 期刊论文
URI标识: http://ir.sia.cn/handle/173321/16700
Appears in Collections:机器人学研究室_期刊论文

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