Missing-Data Classification with the Extended Full-Dimensional Gaussian Mixture Model: Applications to EMG-Based Motion Recognition
文献类型：期刊论文
| 作者 | Ding QC(丁其川) ; Han JD(韩建达); Zhao XG(赵新刚); Chen Y(陈洋)
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| 刊名 | IEEE Transactions on Industrial Electronics
 |
| 出版日期 | 2015 |
| 卷号 | 62期号:8页码:4994-5005 |
| 关键词 | Classification electromyography (EMG) Gaussian mixture model (GMM) missing data myoelectric hand |
| ISSN号 | 0278-0046 |
| 产权排序 | 1 |
| 通讯作者 | 丁其川 |
| 中文摘要 | 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. |
| WOS标题词 | Science & Technology ; Technology |
| 类目[WOS] | Automation & Control Systems ; Engineering, Electrical & Electronic ; Instruments & Instrumentation |
| 研究领域[WOS] | Automation & Control Systems ; Engineering ; Instruments & Instrumentation |
| 关键词[WOS] | SUPPORT VECTOR MACHINES ; MYOELECTRIC CONTROL ; FAULT-DIAGNOSIS ; IMPUTATION ; VALUES ; ANALYZERS ; SYSTEM ; SIGNAL |
| 收录类别 | SCI ; EI |
| 语种 | 英语 |
| WOS记录号 | WOS:000357268300033 |
| 源URL | [http://ir.sia.cn/handle/173321/16700]  |
| 专题 | 沈阳自动化研究所_机器人学研究室 |
| 推荐引用方式 GB/T 7714 | Ding QC,Han JD,Zhao XG,et al. Missing-Data Classification with the Extended Full-Dimensional Gaussian Mixture Model: Applications to EMG-Based Motion Recognition[J]. IEEE Transactions on Industrial Electronics,2015,62(8):4994-5005. |
| APA | Ding QC,Han JD,Zhao XG,&Chen Y.(2015).Missing-Data Classification with the Extended Full-Dimensional Gaussian Mixture Model: Applications to EMG-Based Motion Recognition.IEEE Transactions on Industrial Electronics,62(8),4994-5005. |
| MLA | Ding QC,et al."Missing-Data Classification with the Extended Full-Dimensional Gaussian Mixture Model: Applications to EMG-Based Motion Recognition".IEEE Transactions on Industrial Electronics 62.8(2015):4994-5005. |
入库方式： OAI收割
来源：沈阳自动化研究所
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