表面肌电信号的压缩感知稀疏表示分类研究.doc
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表面肌电信号的压缩感知稀疏表示分类研究,2.13万字自己原创的毕业论文，仅在本站独家出售，重复率低，推荐下载使用摘要： 表面肌电信号(surface electromyographic, semg)的模式分类是智能多功能假肢控制的基本问题。当人体肌肉收缩时，通过安放在皮肤表面的电极可采集到肌肉相应动作电位，即表面肌电信号...
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表面肌电信号的压缩感知稀疏表示分类研究
2.13万字
自己原创的毕业论文，仅在本站独家出售，重复率低，推荐下载使用
摘要： 表面肌电信号(Surface Electromyographic, sEMG)的模式分类是智能多功能假肢控制的基本问题。当人体肌肉收缩时，通过安放在皮肤表面的电极可采集到肌肉相应动作电位，即表面肌电信号。表面肌电信号被广泛地应用于肌肉损伤诊断、康复医学及体育运动等方面的研究，其中一项重要应用为利用表面肌电信号控制假肢。本论文基于压缩感知和稀疏表示理论，研究前臂肌肉在展拳、握拳、内旋、外旋四种运动模式下产生的表面肌电信号的特征提取和多类别模式识别方法。首先介绍了表面肌电信号的基础知识和压缩感知的理论框架及特点。然后基于压缩感知理论对多类别表面肌电信号进行稀疏表示，将肌电信号测试样本表示为训练样本集的过完备字典稀疏线性组合，使用随机测量矩阵获取测试样本降维特征量和稀疏表示感知矩阵，应用正交匹配追踪法求取肌电信号测试样本的稀疏解，由冗余误差最小值确定目标归属类，实现对展拳、握拳、内旋、外旋四种运动模式表面肌电信号的稀疏多分类识别。仿真研究表明基于压缩感知理论的随机矩阵降维映射特征提取不依赖于肌电信号模式类别，构造简单，运算快速，具有普适性； 稀疏表示分类法无需组合多个二分类器来实现多类肌电信号识别，且识别率较高。压缩感知与稀疏表示理论在表面肌电信号模式分类中的应用，能有效简化肌电信号特征提取与模式识别过程，为表面肌电信号模式识别研究提供了新的思路和方法。
关键字：表面肌电信号（sEMG) 模式识别 压缩感知 正交匹配追踪 稀疏性
Study on Classification of Surface EMG Signal Based on Compressive Sensing
Abstract： Surface electromyography (Surface Electromyographic, sEMG) pattern classification is the basic problem of multi function control artificial intelligence.When the human body muscle contraction, the skin surface can be placed on the electrode into the muscle action potential acquisition, namely the surface EMG signal.Surface EMG signal is widely used in the diagnosis of muscle injury, rehabilitation and sports and other aspects, one important application for the use of surface EMG signal controlled prosthesis.In this paper, based on the theory of compressed sensing and sparse representation, feature extraction and multi class pattern recognition method of surface electromyography of the forearm muscles in the exhibition fist, fist, pronation external rotation, four motion mode.The paper first introduces the basic knowledge of surface EMG signal and framework and characteristics of the compressed sensing theory.Then the compressed sensing theory are sparse on multiple categories of surface EMG signal based representation, the EMG signal test sample is expressed as a linear combination of the training sample set complete sparse, using the random measurement matrix for obtaining test sample reduction features and sparse representation of sensing matrix, and using orthogonal matching pursuit method for testing the sample dilute of the EMG signal, the minimum of the redundancy error target attributive in order to classify the four movement modes of surface EMG signal. Simulation results show that the random matrix theory of compressed sensing based on dimensionality reduction mapping feature extraction is not dependent on the classification of the EMG pattern , has the advantages of simple structure, fast calculation, universality.Sparse representation classification without the need to combine multiple classifiers to achieve two many kinds of EMG signal recognition, and the recognition rate is high.Compressed sensing and sparse representation theory applied in pattern classification of surface EMG, can effectively simplify the EMG signal feature extraction and pattern recognition process, and provides a new idea and method for the research of pattern recognition of surface EMG signal.
Key word：Surface Electromyographic(sEMG) Pattern Recognition Compressed Sensing Orthogonal Matching Pursuit Sparsity
目录
摘要 I
Abstract II
第1章 绪论 - 1 -
1.1 研究背景和意义 - 1 -
1.2 国内外的研究现状 - 2 -
1.2.1 肌电信号的特征提取 - 2 -
1.2.2 肌电信号的模式分类 - 3 -
1.3 主要研究内容 - 3 -
1.4 小结 - 4 -
第2章 肌电信号的特征提取和分类方法 - 5 -
2.1 表面肌电信号产生原理 - 5 -
2.2 肌电信号的特点简介 - 6 -
2.3 肌电信号的处理方法 - 7 -
2.3.1 肌电信号的常见分析方法 - 7 -
2.3.2 肌电信号的分类方法 - 8 -
2.4 小结 - 9 -
第3章 压缩感知基本理论 - 10 -
3.1 压缩感知的背景 - 10 -
3.2 压缩感知理论 - 11 -
3.2.1 压缩感知理论简介 - 11 -
3.2.2 压缩感知数学模型 - 13 -
3. 2.3 正交匹配追踪法 - 13 -
3.3 压缩感知理论的应用和优点 - 14 -
3.3.1 压缩感知的应用 - 14 -
3.3.2 压缩感知的优点 - 15 -
3.4 基于压缩感知原理的肌电信号的特征提取和模式识别 - 16 -
3.4.1 肌电信号的稀疏表示特征提取 - 16 -
3.4.2 正交匹配追踪法对信号进行模式识别 - 17 -
3.5 小结 - 17 -
第4章 表面肌电信号的压缩感知模式分类 - 18 -
4.1 表面肌电信号的采集 - 18 -
4.2肌电信号的稀疏表示特征提取 - 18 -
4.2.1 肌电信号的稀疏表示 - 18 -
4.2.2 肌电信号的稀疏求解 - 19 -
4.2.3 随机降维映射的稀疏表示分类法 - 19 -
4.3 MATLAB仿真 - 20 -
4.3.1 MATLAB仿真流程 - 2..
2.13万字
自己原创的毕业论文，仅在本站独家出售，重复率低，推荐下载使用
摘要： 表面肌电信号(Surface Electromyographic, sEMG)的模式分类是智能多功能假肢控制的基本问题。当人体肌肉收缩时，通过安放在皮肤表面的电极可采集到肌肉相应动作电位，即表面肌电信号。表面肌电信号被广泛地应用于肌肉损伤诊断、康复医学及体育运动等方面的研究，其中一项重要应用为利用表面肌电信号控制假肢。本论文基于压缩感知和稀疏表示理论，研究前臂肌肉在展拳、握拳、内旋、外旋四种运动模式下产生的表面肌电信号的特征提取和多类别模式识别方法。首先介绍了表面肌电信号的基础知识和压缩感知的理论框架及特点。然后基于压缩感知理论对多类别表面肌电信号进行稀疏表示，将肌电信号测试样本表示为训练样本集的过完备字典稀疏线性组合，使用随机测量矩阵获取测试样本降维特征量和稀疏表示感知矩阵，应用正交匹配追踪法求取肌电信号测试样本的稀疏解，由冗余误差最小值确定目标归属类，实现对展拳、握拳、内旋、外旋四种运动模式表面肌电信号的稀疏多分类识别。仿真研究表明基于压缩感知理论的随机矩阵降维映射特征提取不依赖于肌电信号模式类别，构造简单，运算快速，具有普适性； 稀疏表示分类法无需组合多个二分类器来实现多类肌电信号识别，且识别率较高。压缩感知与稀疏表示理论在表面肌电信号模式分类中的应用，能有效简化肌电信号特征提取与模式识别过程，为表面肌电信号模式识别研究提供了新的思路和方法。
关键字：表面肌电信号（sEMG) 模式识别 压缩感知 正交匹配追踪 稀疏性
Study on Classification of Surface EMG Signal Based on Compressive Sensing
Abstract： Surface electromyography (Surface Electromyographic, sEMG) pattern classification is the basic problem of multi function control artificial intelligence.When the human body muscle contraction, the skin surface can be placed on the electrode into the muscle action potential acquisition, namely the surface EMG signal.Surface EMG signal is widely used in the diagnosis of muscle injury, rehabilitation and sports and other aspects, one important application for the use of surface EMG signal controlled prosthesis.In this paper, based on the theory of compressed sensing and sparse representation, feature extraction and multi class pattern recognition method of surface electromyography of the forearm muscles in the exhibition fist, fist, pronation external rotation, four motion mode.The paper first introduces the basic knowledge of surface EMG signal and framework and characteristics of the compressed sensing theory.Then the compressed sensing theory are sparse on multiple categories of surface EMG signal based representation, the EMG signal test sample is expressed as a linear combination of the training sample set complete sparse, using the random measurement matrix for obtaining test sample reduction features and sparse representation of sensing matrix, and using orthogonal matching pursuit method for testing the sample dilute of the EMG signal, the minimum of the redundancy error target attributive in order to classify the four movement modes of surface EMG signal. Simulation results show that the random matrix theory of compressed sensing based on dimensionality reduction mapping feature extraction is not dependent on the classification of the EMG pattern , has the advantages of simple structure, fast calculation, universality.Sparse representation classification without the need to combine multiple classifiers to achieve two many kinds of EMG signal recognition, and the recognition rate is high.Compressed sensing and sparse representation theory applied in pattern classification of surface EMG, can effectively simplify the EMG signal feature extraction and pattern recognition process, and provides a new idea and method for the research of pattern recognition of surface EMG signal.
Key word：Surface Electromyographic(sEMG) Pattern Recognition Compressed Sensing Orthogonal Matching Pursuit Sparsity
目录
摘要 I
Abstract II
第1章 绪论 - 1 -
1.1 研究背景和意义 - 1 -
1.2 国内外的研究现状 - 2 -
1.2.1 肌电信号的特征提取 - 2 -
1.2.2 肌电信号的模式分类 - 3 -
1.3 主要研究内容 - 3 -
1.4 小结 - 4 -
第2章 肌电信号的特征提取和分类方法 - 5 -
2.1 表面肌电信号产生原理 - 5 -
2.2 肌电信号的特点简介 - 6 -
2.3 肌电信号的处理方法 - 7 -
2.3.1 肌电信号的常见分析方法 - 7 -
2.3.2 肌电信号的分类方法 - 8 -
2.4 小结 - 9 -
第3章 压缩感知基本理论 - 10 -
3.1 压缩感知的背景 - 10 -
3.2 压缩感知理论 - 11 -
3.2.1 压缩感知理论简介 - 11 -
3.2.2 压缩感知数学模型 - 13 -
3. 2.3 正交匹配追踪法 - 13 -
3.3 压缩感知理论的应用和优点 - 14 -
3.3.1 压缩感知的应用 - 14 -
3.3.2 压缩感知的优点 - 15 -
3.4 基于压缩感知原理的肌电信号的特征提取和模式识别 - 16 -
3.4.1 肌电信号的稀疏表示特征提取 - 16 -
3.4.2 正交匹配追踪法对信号进行模式识别 - 17 -
3.5 小结 - 17 -
第4章 表面肌电信号的压缩感知模式分类 - 18 -
4.1 表面肌电信号的采集 - 18 -
4.2肌电信号的稀疏表示特征提取 - 18 -
4.2.1 肌电信号的稀疏表示 - 18 -
4.2.2 肌电信号的稀疏求解 - 19 -
4.2.3 随机降维映射的稀疏表示分类法 - 19 -
4.3 MATLAB仿真 - 20 -
4.3.1 MATLAB仿真流程 - 2..
