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题名: 基于肌电信号容错分类的手部动作识别
其他题名: Recognizing hand motions based on fault-tolerant classification with EMG signals
作者: 丁其川; 赵新刚; 韩建达
作者部门: 机器人学研究室
关键词: 肌电信号 ; 数据丢失 ; 动作分类 ; 人机交互
刊名: 机器人
ISSN号: 1002-0446
出版日期: 2015
卷号: 37, 期号:1, 页码:9-16
收录类别: EI ; CSCD
产权排序: 1
项目资助者: 国家自然科学基金资助项目(61273355,61273356,61035005)
摘要: 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。
英文摘要: In 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.
语种: 中文
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内容类型: 期刊论文
URI标识: http://ir.sia.cn/handle/173321/16212
Appears in Collections:机器人学研究室_期刊论文

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