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基于肌电信号容错分类的手部动作识别
Alternative TitleRecognizing hand motions based on fault-tolerant classification with EMG signals
丁其川; 赵新刚; 韩建达
Department机器人学研究室
Source Publication机器人
ISSN1002-0446
2015
Volume37Issue:1Pages:9-16
Indexed ByEI ; CSCD
EI Accession number20152000855335
CSCD IDCSCD:5364506
Contribution Rank1
Funding Organization国家自然科学基金资助项目(61273355,61273356,61035005)
Keyword肌电信号 数据丢失 动作分类 人机交互
Abstract针对肌电交互系统中因电极断开、损坏及数据传输中断等故障造成的数据错误/丢失问题,提出一种基于高斯混合模型的肌电信号容错分类方法,通过对肌电信号特征样本中错误/丢失数据边缘化或条件均值归错实现非完整数据样本分类。应用所提出的方法识别5种手部动作,实验结果表明,该方法的动作识别精度要高于传统的零归错与均值归错方法。最后,融合容错分类机制开发了肌电假手平台,在线实验验证了提出的方法可以有效提高肌电交互系统的鲁棒性。
Other AbstractIn view of the fault/missing data problem caused by disconnected/damaged electrodes and data-transmission interrupting in myoelectric-interface systems, an EMG (electromyography) fault-tolerant classification method based on Gaussian mixture model is proposed, with which an incomplete-data sample can be classified via marginalizing or conditionalmean imputation of the fault/missing data in the EMG feature sample. The proposed method is applied to recognizing five kinds of hand motion. Experimental results show that the proposed method can provide higher motion-recognition accuracy than that by the traditional zero and mean imputation methods. Finally, a myoelectric-hand platform is developed by involving the fault-tolerant classification mechanism, and the online experiments show that the proposed method can effectively improve the robustness of myoelectric-interface systems.
Language中文
Citation statistics
Cited Times:4[CSCD]   [CSCD Record]
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/16212
Collection机器人学研究室
Affiliation1.中国科学院沈阳自动化研究所机器人学国家重点实验室
2.中国科学院大学
Recommended Citation
GB/T 7714
丁其川,赵新刚,韩建达. 基于肌电信号容错分类的手部动作识别[J]. 机器人,2015,37(1):9-16.
APA 丁其川,赵新刚,&韩建达.(2015).基于肌电信号容错分类的手部动作识别.机器人,37(1),9-16.
MLA 丁其川,et al."基于肌电信号容错分类的手部动作识别".机器人 37.1(2015):9-16.
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