基于小波特征分析的手指动作识别研究

目的:本文利用表面肌电(sEMG)信号来研究多种手指组合动作的识别问题。方法:在对采集的四个通道sEMG信号进行降噪预处理的基础上,采用移动加窗处理方法来提取关于手指运动状态的信号活动段,再分析各个信号活动段的小波系数统计特征,进而利用多类支持向量机(SVM)分类算法来实现手指组合动作的识别。结果:动作识别率最高达到100%。结论:所采用方法能够有效地识别多种手势动作,并为后续基于肌电信号的实时人机接口系统的研究奠定了理论基础。     

表面肌电信号采集与降噪处理

目的：本文以设计的表面~g（sEMG）信号采集系统为基础,探讨sEMG信号中的降噪处理问题。方法：结合sEMG信号的噪声影响情况,首先利用带通滤波器消除肌电信号频带外噪声,再通过频谱插值法来抑制工频干扰分量,最后使用小波分析方法来削弱肌电信号频带内噪声。结果：通过对检测sEMG信号的降噪处理,信号噪声得到明显抑制。结论：所设计采集系统能够获得满意的sEMG信号检测效果,所采用降噪方法能够有效提高sEMG信号的质量。     

基于重采样技术的肌电信号动作电位传导速度估计研究

目的：本文针对表面肌电（sEMG）信号探讨动作电位传导速度（APCV）估计问题。方法：以生理学仿真sEMG信号为基础,采用基于互相关分析的时延估计技术来获取相应的APCV估计值,并利用重采样技术来提高估计的精度。结果：实验表明。针对重采样后的仿真信号,其APCV的估计误差得到了明显降低。结论：所采用方法能够有效获取满意的APCV估计效果。     

表面肌电信号采集与降噪处理

目的:本文以设计的表面肌电(sEMG)信号采集系统为基础,探讨sEMG信号中的降噪处理问题。方法:结合sEMG信号的噪声影响情况,首先利用带通滤波器消除肌电信号频带外噪声,再通过频谱插值法来抑制工频干扰分量,最后使用小波分析方法来削弱肌电信号频带内噪声。结果:通过对检测sEMG信号的降噪处理,信号噪声得到明显抑制。结论:所设计采集系统能够获得满意的sEMG信号检测效果,所采用降噪方法能够有效提高sEMG信号的质量。     

基于重采样技术的肌电信号动作电位传导速度估计研究

目的:本文针对表面肌电(sEMG)信号探讨动作电位传导速度(APCV)估计问题。方法:以生理学仿真sEMG信号为基础,采用基于互相关分析的时延估计技术来获取相应的APCV估计值,并利用重采样技术来提高估计的精度。结果:实验表明,针对重采样后的仿真信号,其APCV的估计误差得到了明显降低。结论:所采用方法能够有效获取满意的APCV估计效果。     

基于支持向量机的表面肌电信号和加速度融合跌倒识别方法

目的:针对老人易跌倒和跌倒过后可能产生严重后果这一现实问题,通过将表面肌电信号和加速度融合,进一步优化采用支持向量机分类器下的包含跌倒在内的几种不同动作的分类效果。方法:提出基于表面肌电和加速度信号融合的跌倒识别算法,首先采集股直肌,股内侧肌,胫骨前肌和腓肠肌的表面肌电信号以及位于腰部的三轴加速度信号作为实验数据,然后利用滑动窗口法提取表面肌电和加速度信号的均方根值,最后针对人体日常活动和跌倒的运动特征,构建了支持向量机的分类器。结果:实验数据共计320组数据,包括3种日常活动和向前跌倒,其中160组数据作为训练集,另外160组数据作为测试集。对4种动作进行识别实验,算法的准确度为93.23%、灵敏度为92.4%、特异度为100%,达到了良好的分类效果。结论:基于支持向量机的表面肌电信号和加速度融合的跌倒识别算法分类效果良好,对于老人跌倒防护具有现实意义。     

等长收缩诱发肌肉疲劳及恢复过程中表面肌电信号特征变化规律

运用线性和非线性分析方法分析不同强度等长收缩诱发局部肌肉疲劳及恢复过程中表面肌电信号（surface electromyogram,sEMG）特征的变化规律,探讨影响sEMG信号变化的可能原因和机制.结果显示,在肱二头肌疲劳收缩过程中,sEMG的特征指标平均肌电值（average EMG,AEMG）、平均功率频率（mean power frequency,MPF）、Lempel-Ziv复杂度（Lempel-Ziv complexity,C（n））和确定性线段百分数（Determinism%,% DET）的变化具有良好的规律性.恢复期AEMG没有表现出规律性的变化,MPF、C（n）和?T在恢复期2秒即开始显著恢复,在前10秒恢复很快,随后恢复速度变慢.恢复初期sEMG信号特征的快速变化提示中枢控制因素可能发挥更大作用.     

局部肌肉疲劳的表面肌电信号复杂度和熵变化

目的 在于探讨静态和动态疲劳性运动过程中肱二头肌和腰部脊竖肌表面肌电(surface electromyography,sEMG)信号的Lempel-Ziv复杂度和Kolmogorov熵的变化规律。18名男性大学生志愿者被随机分为肱二头肌和腰部脊竖肌运动负荷组,分别完成静态和动态疲劳运动负荷试验。运动负荷期间连续记录sEMG信号,在对运动负荷时间和重复次数进行标准化处理后,截取相应时段的sEMG信号,计算Lempel-Ziv复杂度和Kolmogorov熵,观察它们随肌肉疲劳发展的变化规律。研究结果表明,无论是静态还是动态疲劳运动条件下,被检肌肉sEMG信号的复杂度和熵均随着运动负荷时间呈现明显的单调递减型变化。该变化可能与神经系统渐进性协调众多运动单位同步收缩的‘协同效应”有关。     

基于定量分析方法的动作表面肌电信号分析

介绍了非线性数据处理方法递归图法(recurrence plots,RP)及其定量分析方法(recurrence quantifi-cation analysis,RQA),并利用RP和RQA研究了动作表面肌电信号。研究发现,表面肌电信号在不同动作模式下其所对应的RP图在结构上差异明显,通过计算两通道肌电信号的RQA指标递归率,发现不同动作信号的RQA指标递归率值具有不同的聚类分布。该方法为肌电信号的动作模式分类提供了一种新的思路。     

基于思维脑电信号的假手的研究

本文主要研究利用思维脑电信号来控制假手动作。采用小波变换对思维脑电信号进行分解,选取合适的子带信号并提取相应能量特征,组成特征向量输入BP神经网络进行分类识别。整个信号处理过程在LabVIEW软件平台上实现,并利用其串口通信模块输出控制指令来控制假手的张开和闭合。     

Pattern recognition of number gestures based on a wireless surface EMG system

Using surface electromyography (sEMG) signal for efficient recognition of hand gestures has attracted increasing attention during the last decade, with most previous work being focused on recognition of upper arm and gross hand movements and some work on the classification of individual finger movements such as finger typing tasks. However, relatively few investigations can be found in the literature for automatic classification of multiple finger movements such as finger number gestures. This paper focuses on the recognition of number gestures based on a 4-channel wireless sEMG system. We investigate the effects of three popular feature types (i.e. Hudgins’ time–domain features (TD), autocorrelation and cross-correlation coefficients (ACCC) and spectral power magnitudes (SPM)) and four popular classification algorithms (i.e. k-nearest neighbor (k-NN), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA) and support vector machine (SVM)) in offline recognition. Motivated by the good performance of SVM, we further propose combining the three features and employing a new classification method, multiple kernel learning SVM (MKL-SVM). Real sEMG results from six subjects show that all combinations, except k-NN or LDA using ACCC features, can achieve above 91% average recognition accuracy, and the highest accuracy is 97.93% achieved by the proposed MKL-SVM method using the three feature combination (3F). Referring to the offline recognition results, we also implement a real-time recognition system. Our results show that all six subjects can achieve a real-time recognition accuracy higher than 90%. The number gestures are therefore promising for practical applications such as human–computer interaction (HCI).     

Surface EMG pattern recognition for real-time control of a wrist exoskeleton

Background  

Surface electromyography (sEMG) signals have been used in numerous studies for the classification of hand gestures and movements and successfully implemented in the position control of different prosthetic hands for amputees. sEMG could also potentially be used for controlling wearable devices which could assist persons with reduced muscle mass, such as those suffering from sarcopenia. While using sEMG for position control, estimation of the intended torque of the user could also provide sufficient information for an effective force control of the hand prosthesis or assistive device. This paper presents the use of pattern recognition to estimate the torque applied by a human wrist and its real-time implementation to control a novel two degree of freedom wrist exoskeleton prototype (WEP), which was specifically developed for this work.     

Estimation of independent and dependent components of non-invasive EMG using fast ICA: validation in recognising complex gestures

The identification of a number of active muscles during complex actions is the useful information to identify different gestures. Biosignals such as surface electromyogram (sEMG) are a result of the summation of electrical activity of a number of sources. The complexity of the anatomy and actions makes it difficult in identifying the number of active sources from the multiple channel recordings. This paper addresses two applications of independent component analysis (ICA) on sEMG: the first one is to evaluate the use of ICA for the separation of bioelectric signals when the number of active sources may not be known. The second application is to identify complex hand gestures using decomposed sEMG. The theoretical analysis and experimental results demonstrate that the ICA is suitable for the separation of myoelectric signals. The results identify the usage of ICA for identifying complex gestures.     

Estimation of independent and dependent components of non-invasive EMG using fast ICA: validation in recognising complex gestures

The identification of a number of active muscles during complex actions is the useful information to identify different gestures. Biosignals such as surface electromyogram (sEMG) are a result of the summation of electrical activity of a number of sources. The complexity of the anatomy and actions makes it difficult in identifying the number of active sources from the multiple channel recordings. This paper addresses two applications of independent component analysis (ICA) on sEMG: the first one is to evaluate the use of ICA for the separation of bioelectric signals when the number of active sources may not be known. The second application is to identify complex hand gestures using decomposed sEMG. The theoretical analysis and experimental results demonstrate that the ICA is suitable for the separation of myoelectric signals. The results identify the usage of ICA for identifying complex gestures.     

Quantifying Forearm Muscle Activity during Wrist and Finger Movements by Means of Multi-Channel Electromyography

The study of hand and finger movement is an important topic with applications in prosthetics, rehabilitation, and ergonomics. Surface electromyography (sEMG) is the gold standard for the analysis of muscle activation. Previous studies investigated the optimal electrode number and positioning on the forearm to obtain information representative of muscle activation and robust to movements. However, the sEMG spatial distribution on the forearm during hand and finger movements and its changes due to different hand positions has never been quantified. The aim of this work is to quantify 1) the spatial localization of surface EMG activity of distinct forearm muscles during dynamic free movements of wrist and single fingers and 2) the effect of hand position on sEMG activity distribution. The subjects performed cyclic dynamic tasks involving the wrist and the fingers. The wrist tasks and the hand opening/closing task were performed with the hand in prone and neutral positions. A sensorized glove was used for kinematics recording. sEMG signals were acquired from the forearm muscles using a grid of 112 electrodes integrated into a stretchable textile sleeve. The areas of sEMG activity have been identified by a segmentation technique after a data dimensionality reduction step based on Non Negative Matrix Factorization applied to the EMG envelopes. The results show that 1) it is possible to identify distinct areas of sEMG activity on the forearm for different fingers; 2) hand position influences sEMG activity level and spatial distribution. This work gives new quantitative information about sEMG activity distribution on the forearm in healthy subjects and provides a basis for future works on the identification of optimal electrode configuration for sEMG based control of prostheses, exoskeletons, or orthoses. An example of use of this information for the optimization of the detection system for the estimation of joint kinematics from sEMG is reported.     

The application of independent component analysis to the multi-channel surface electromyographic signals for separation of motor unit action potential trains: part I-measuring techniques.

The purpose of this study is to examine whether or not the application of independent component analysis (ICA) is useful for separation of motor unit action potential trains (MUAPTs) from the multi-channel surface EMG (sEMG) signals. In this study, the eight-channel sEMG signals were recorded from tibialis anterior muscles during isometric dorsi-flexions at 5%, 10%, 15% and 20% maximal voluntary contraction. Recording MUAP waveforms with little time delay mounted between the channels were obtained by vertical sEMG channel arrangements to muscle fibers. The independent components estimated by FastICA were compared with the sEMG signals and the principal components calculated by principal component analysis (PCA). From our results, it was shown that FastICA could separate groups of similar MUAP waveforms of the sEMG signals separated into each independent component while PCA could not sufficiently separate the groups into the principal components. A greater reduction of interferences between different MUAP waveforms was demonstrated by the use of FastICA. Therefore, it is suggested that FastICA could provide much better discrimination of the properties of MUAPTs for sEMG signal decomposition, i.e. waveforms, discharge intervals, etc., than not only PCA but also the original sEMG signals.     

Reliability of surface EMG matrix in locating the innervation zone of upper trapezius muscle

The identification of the motor unit (MU) innervation zone (IZ) using surface electromyographic (sEMG) signals detected on the skin with a linear array or a matrix of electrodes has been recently proposed in the literature. However, an analysis of the reliability of this procedure and, therefore, of the suitability of the sEMG signals for this purpose has not been reported.The purpose of this work is to describe the intra and inter-rater reliability and the suitability of surface EMG in locating the innervation zone of the upper trapezius muscle.Two operators were trained on electrode matrix positioning and sEMG signal analysis. Ten healthy subjects, instructed to perform a series of isometric contractions of the upper trapezius muscle participated in the study. The two operators collected sEMG signals and then independently estimated the IZ location through visual analysis.Results showed an almost perfect agreement for intra-rater and inter-rater reliability. The constancy of IZ location could be affected by the factors reflecting the population of active MUs and their IZs, including: the contraction intensity, the acquisition period analyzed, the contraction repetition. In almost all cases the IZ location shift due to these factors did not exceed 4 mm. Results generalization to other muscles should be made with caution.     

The application of independent component analysis to the multi-channel surface electromyographic signals for separation of motor unit action potential trains: part II-modelling interpretation.

The purpose of this article was to investigate whether or not FastICA can separate identical motor unit action potential trains (MUAPTs) of the 8-channel surface electromyographic (sEMG) signals constructed by an sEMG model into the independent components. Firstly, we have examined how much the increase of motor units (MUs) in the simulated sEMG signals influenced the performance on the separation of MUAPTs by kurtosis. The decreased trend of mean kurtosis on both sEMG signals and their independent components were observed as MUs were increased. These data suggested that the separation performance decayed when MUs were increased. Secondary, the differences between the independent components and the principal components have been also applied to the simulated sEMG signals with or without time delay between the sEMG channels. FastICA could successfully separate identical MUAPTs with no time delay but principal component analysis (PCA) could not do so. Against it, both FastICA and PCA could not separate MUAPTs with some time delay. In conclusion, our results suggested that FastICA could separate identical MUAPTs with no time delay into the independent components by FastICA, which might offer a new technique for the separation of interfered MUAP waveforms based on statistical properties of sEMG signal distributions.