基于肌电信号容错分类的手部动作识别
Recognizing Hand Motions Based on Fault-tolerant Classification with EMG Signals
查看参考文献17篇
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文摘
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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. |
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其他语种文摘
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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 conditional-mean 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. |
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来源
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机器人
,2015,37(1):9-16 【核心库】
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DOI
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10.13973/j.cnki.robot.2015.0009
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关键词
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肌电信号
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数据丢失
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动作分类
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人机交互
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地址
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中国科学院沈阳自动化研究所, 机器人学国家重点实验室, 辽宁, 沈阳, 110016
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语种
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中文 |
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文献类型
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研究性论文 |
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ISSN
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1002-0446 |
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学科
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自动化技术、计算机技术 |
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基金
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国家自然科学基金资助项目
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文献收藏号
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CSCD:5364506
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