Classification of surface EMG signal with fractal dimension

Surface EMG (electromyography) signal is a complex nonlinear signal with low signal to noise ratio (SNR). This paper is aimed at identifying different patterns of surface EMG signals according to fractal dimension. Two patterns of surface EMG signals are respectively acquired from the right forearm flexor of 30 healthy volunteers during right forearm supination (FS) or forearm pronation (FP). After the high frequency noise is filtered from surface EMG signal by a low-pass filter, fractal di-mension is calculated from the filtered surface EMG signal. The results showed that the fractal dimensions of filtered FS surface EMG signals and those of filtered FP surface EMG signals distribute in two different regions, so the fractal dimensions can rep-resent different patterns of surface EMG signals.

The Change of Spectral Energy Distribution of Surface EMG Signal During Forearm Action Process

Spectral energy distribution of surface EMG signal is often used but difficultly and effectively control artificial limb, because the spectral energy distribution changes in the process of limb actions. In this paper, the general characteristics of surface EMG signal patterns were firstly characterized by spectral energy change. 13 healthy subjects were instructed to execute forearm supination （FS） and forearm pronation （FP） with their right foreanns when their forearm muscles were ＂fatigue＂ or ＂relaxed＂. All surface EMG signals were recorded from their right forearm flexor during their right forearm actions. Two sets of surface EMG signals were segmented from every surface EMG signal appropriately at preparing stage and acting stage. Relative wavelet packet energy （symbolized by pnp and pna respectively at preparing stage and acting stage, n denotes the nth frequency band） of surface EMG signal firstly was calculated and then, the difference （Pn = Pna-Pnp） were gained. The results showed that Pn from some frequency bands can effectively characterize the general characteristics of surface EMG signal patterns. Compared with Pn in other frequency bands, P4, the spectral energy change from 93.75 to 125 Hz, was more appropriately regarded as the features.

Characterization of surface EMG signals using improved approximate entropy

An improved approximate entropy (ApEn) is presented and applied to characterize surface electromyography (sEMG) signals. In most previous experiments using nonlinear dynamic analysis, this certain processing was often confronted with the problem of insufficient data points and noisy circumstances, which led to unsatisfactory results. Compared with fractal dimension as well as the standard ApEn, the improved ApEn can extract information underlying sEMG signals more efficiently and accu- rately. The method introduced here can also be applied to other medium-sized and noisy physiological signals.

Classification of uterine EMG signals using supervised classification method

Aim: The main purpose of this article is to detect any risk of preterm deliveries at an early gestation period using uterine electromyography signals. Detecting such uterine signals can yield a promising approach to determine and take actions to prevent this potential risk. Methods: The best position for the detection of different uterine signals is the median vertical axis of the abdomen. These signals differ from each other by their frequency content. Initially, simulation is done for the real detected EMG signals: preterm deliveries (PD) EMGs and deliveries at term (DT) EMGs. This is performed by applying autoregressive model (AR) of specific order to estimate AR coefficients of these real EMG signals. Finally, after calculation of the AR parameters of the two types of deliveries, we generate two types of simulated uterine contractions by using White Gaussian Noise (WGN). Frequency parameter extraction and classification are first applied on simulated signals to test the limits and performance of the used methods. The last remaining step is the classification of the contractions using supervised classification method. Results: Results show that uterine contractions may be classified using the Artificial Neural Networks (ANNs). The Simple Perceptron ANN is applied on the signals for their supervised classification into independent groups: preterm deliveries (PD) and deliveries at term (TD) according to their frequency content.

Feature Layer Fusion of Linear Features and Empirical Mode Decomposition of Human EMG Signal

To explore the influence of the fusion of different features on recognition,this paper took the electromyography(EMG)signals of rectus femoris under different motions(walk,step,ramp,squat,and sitting)as samples,linear features(time-domain features(variance(VAR)and root mean square(RMS)),frequency-domain features(mean frequency(MF)and mean power frequency(MPF)),and nonlinear features(empirical mode decomposition(EMD))of the samples were extracted.Two feature fusion algorithms,the series splicing method and complex vector method,were designed,which were verified by a double hidden layer(BP)error back propagation neural network.Results show that with the increase of the types and complexity of feature fusions,the recognition rate of the EMG signal to actions is gradually improved.When the EMG signal is used in the series splicing method,the recognition rate of time-domain+frequency-domain+empirical mode decomposition(TD+FD+EMD)splicing is the highest,and the average recognition rate is 92.32%.And this rate is raised to 96.1%by using the complex vector method,and the variance of the BP system is also reduced.

基于sEMG的手指康复治疗的信号处理研究

手指功能在日常生活中特别重要,特别是在一些需要抓取和一些较为精细的动作中,对日常生活质量有着不可忽视的影响。而目前临床针对手指功能康复的治疗模式主要采用辅助设备康复,而这种模式又较为枯燥。提出了一种通过采集表面肌电信号(sEMG)的方式,使得手指功能受损的患者可以脱离现有比较枯燥的治疗方式,同时也更有利于患者其他功能例如神经系统功能的恢复。采用肌电信号公开数据集,对原始肌电信号做相关的预处理,同时采用matlab仿真的方式验证预处理的正确性;并通过临床实验采集患者肌电信号的方式验证使用目前的肌电传感器对运动意图分析的可行性。

基于sEMG的快递职业上装与肌肉疲劳度关系的研究

为了研究快递员职业上装与肌肉疲劳度之间的关系,以顺丰速运有限公司的夏、秋两套职业装为例,招募了7名男性受试者来模拟快递员的行为特征,测量了受试者工作状态下指伸肌、肱二头肌长头、斜方肌3个肌群的表面肌电信号,运用统计学分析方法分析了工作状态中的肌肉疲劳特征。在模拟快递员工作的实验中,结合受试者的主观分析及表面肌电信号数据可知,肱二头肌长头相比其他测试部位肌肉疲劳感最为强烈,穿着样衣2^(#)时的疲劳感比穿着样衣1^(#)时显著增加(p<0.05)。研究结果可为快递员职业上装的版型设计提供参考。

肌电信号控制的智能小车实验平台设计

肌电信号是人体肌群在运动时产生的一种微弱信号,该信号蕴藏着与运动相关的控制信息源。提出了一种基于肌电信号的智能小车控制系统。该系统由肌电信号采集模块、无线传输模块、小车控制模块和显示模块等组成。整个系统分为主从两部分。主机采用STM32F103ZET6微处理器对肌电信号进行多通道采集,提取所采集信号的特征值。将特征值分为测试集和训练集,并对不同手势信号贴上对应的标签,使用K最近邻(KNN)算法对测试集进行准确度分析以实现对不同手势的识别。识别结果通过无线传输模块发送给从机小车,小车接收到主机发送的内容后进行相应的动作。测试结果表明,所提出的方法在不同时间段信号采集的平均准确率可达91.14%以上,系统具有很好的鲁棒性。

基于表面肌电信号及肌肉疲劳的上肢肌力预测

为解决目前肌肉力测量时用肢体末端力表示实际肌肉力大小,以及未将肌肉疲劳程度考虑在内的问题,本文提出了一种基于表面肌电信号和肌肉疲劳的上肢肌肉力预测方法。利用AnyBody软件建立上肢肌肉骨骼模型,并将上肢末端力经过仿真得到单块肌肉的肌力大小;采用肌肉等长收缩的时间来表征肌肉疲劳程度。10名健康男性受试者进行上肢等长收缩实验,提取实验过程中肱二头肌肌电信号的积分肌电值、均方根、中值频率、平均功率频率、最大小波系数及其对应频率六个特征值;将肌肉力与特征值、肌肉疲劳程度进行分析后发现三者之间高度相关。采用麻雀搜索算法优化BP神经网络的权值和阈值,构造并训练上肢肌力预测模型。经测试集检验结果表明,该方法的误差小于12%,可以对肌力进行较为准确的预测。

多源域迁移学习的肌电-惯性特征融合及手势识别

在跨用户手势识别研究中,针对单源域迁移学习存在的负迁移和模型泛化性能差的问题,本研究创新性地提出了一种基于肌电-惯性特征融合的多源域迁移学习策略,关键创新点在于整合多个源域的数据,并在此基础上采用域特有特征对齐与域分类器对齐的技术手段。这一方法旨在强化模型在不同用户间的手势识别性能,进而显著提升跨用户手势识别系统的准确性。首先,引入长短时记忆(long short-term memory, LSTM)网络模型,提取肌电-惯性信息的平均绝对值、方差、峰值等时序特征;其次进行域特有特征对齐与域分类器对齐,利用多个源域数据完成对目标域的特征提取;最后融合分类损失、域特有特征差异损失和域分类器差异损失3个损失函数,协同优化整体损失。实验结果表明,所提方法与单源域、源域组合等多种传统方法相比,识别平均率有所提高,在NinaPro DB5数据集上,目标用户的手势识别平均准确率达到80%以上。

基于表面肌电信号对乒乓球运动员挥拍动作的特征研究

通过对乒乓球运动员进行采集上肢表面肌电信号数据的实验,分析其在进行挥拍动作时的肌肉活动特征。实验对象为30名具有一定乒乓球训练经验的运动员,分别进行正手挥拍、反手挥拍的实验任务。通过表面肌电信号采集设备记录运动员挥拍时肌肉的电活动信号,并对信号进行预处理和分析,提取出肌肉活动的特征参数。利用BP神经网络算法比较表面肌电信号时域特征、频域特征以及时频特征对挥拍动作的影响及识别预测。通过比较肌肉活动特征,探讨乒乓球运动员在不同动作中肌肉协调和活动强度的变化,实验中可正确模拟采集上肢挥拍规律,在运动员基础训练中具有应用价值。

基于多模态轻量化混合模型的情绪识别

实现更加准确的情绪识别是当前面临的一项富含挑战性且十分有意义的任务。由于情绪的复杂多样性,单一模态的脑电信号难以对情绪进行全面客观的度量。因此本文提出一种多模态轻量化混合模型PCA-MWReliefFGAPSO-SVM,该混合模型由PCA-MWReliefF特征通道选择器和GAPSO-SVM分类器构成。选用脑电信号(EEG)、肌电信号(EMG)、体温信号(TEM)三模态信号进行情绪识别。在DEAP公共数据集上进行多次实验验证,在效价维度、唤醒维度和四分类中分别取得了97.5000%、95.8333%、95.8333%的分类准确率。实验结果表明,提出的混合模型有助于提高情绪识别准确率且明显优于单模态情绪识别。与近期的类似工作相比,本文提出的混合模型具有较高准确率、计算量小且通道数少的优点,更易于实际应用。

基于生物力学和颈腰部EMG判别驾驶员疲劳状态

本文中通过采用颈腰部生物力学和表面肌电信号相结合的方式,对驾驶员在驾驶过程中的疲劳状态进行了研究。首先,通过生物力学的计算与分析,合理地选择了能有效反映驾驶疲劳状态的生理信号采集位置,即颈6左右两侧上斜方肌和腰4左右两侧竖脊肌。然后,在利用经验模态分解算法对测得的肌电信号进行去噪的基础上,找出能表征驾驶员疲劳状态的颈腰部肌电特性参数,并对提取的特征参数(颈部复杂度、腰部复杂度和腰部近似熵)进行主成分分析,获得了两个主成分,有效保留有用信息,去除冗余信息,实现了特征参数的降维。最后,以此为自变量建立疲劳驾驶评价模型,有效提高了模型的正确率,加快了模型的运算速度。结果表明,该方法在对驾驶员正常与疲劳状态的区分上具有良好的识别效果,正确率可达90%以上。

划船运动员静力及动力性肌肉运动疲劳时肌氧含量的变化特征及对EMG参数的影响

研究目的:1)研究静力和动力性肌肉运动疲劳时肌肉氧含量的变化特点及其规律;2)了解静力性、动力性递增强度运动时EMG参数变化;3)探讨肌氧含量与EMG参数变化之间的关系,为肌肉疲劳时影响肌电-肌氧机制提供可能的理论基础。研究方法:1)肌氧含量的测试:探头纵向旋转让光源和检测器的轴线平行于股外侧肌外侧头大腿测定运动时肌氧含量的变化;2)肌电的测试:采用表面肌电图的测量,得出表面电图各指标参数;3)静力负荷等长收缩:通过力量传感器测出其最大肌肉收缩所对应的MVC;4)动力性负荷运动:采取功率自行车逐级递增负荷的测试方法作为肌肉的动力性运动,同步记录EMG参数,并在每一级负荷末30s采血测定血乳酸浓度。结论:静力性运动时E/T值大幅度增大的时间大多出现在肌氧停止下降之后,肌氧的降低程度与肌肉疲劳程度有关。动力性运动时血乳酸值随负荷而增加,IEMG的变化趋势与血乳酸相似,IEMG、Oxy-Hb、BL值三者呈非常显著性相关,表明肌肉疲劳时肌肉氧供和内环境的改变也是影响EMG信号的重要因素。

益肾通痹方对膝骨关节炎患者股四头肌sEMG的影响及其临床疗效研究

目的:探讨益肾通痹方对膝骨关节炎患者的股四头肌表面肌电信号的影响及其治疗作用。方法 :选择我院2016年3月—2017年12月收治的80例膝骨关节炎患者,按照随机数字表法分为对照组(38例)和治疗组(42例),对照组给予双醋瑞因胶囊,治疗组给予益肾通痹方,用膝关节HSS评分、膝关节功能KSFS评分及Lequensne评分指数评价两组患者治疗效果,同时测定两组治疗前后股四头肌表面肌电(sEMG)信号变化及不良反应,包括股直肌(RF)、骨外侧肌(VL)与骨内侧肌(VMO)的积分肌电值(IEMG)和中位频率(MF)。结果 :治疗组患者有效率78.6%,高于对照组65.8%(P<0.05);治疗组患者治疗后较治疗前RF、VL、VMO的IEMG与MF增强幅度显著大于对照组(P<0.05);两组患者无明显不良反应。结论:益肾通痹方治疗膝骨关节炎疗效显著,对股四头肌表面肌电活动有促进作用,能提高RF、VMO与VL的肌力及耐疲劳性,增强股四头肌表面肌电活动信号。

多视角手部肌肉疲劳动作智能识别方法仿真

干扰信号工频和谐波频率噪声会影响动作识别效果,为了提升手部肌肉的识别精度,提出基于肌电信号的多视角手部肌肉疲劳动作识别方法。利用肌电信号采集系统采集手部肌肉疲劳动作肌电信号,利用空域相关滤波算法优化肌电信号,消除干扰信号工频和谐波频率的生理噪声,提升动作识别精度。从时域和频域两个角度出发提取肌电信号特征,并输入支持向量机中,根据支持向量机的分类结果,实现多视角手部肌肉疲劳动作识别。实验结果表明,所提方法识别性能较好、识别精度较高,能够有效提升多视角下手部肌肉疲劳动作识别效果。

改进BP神经网络的EMG手指运动识别

在基于肌电信号(EMG)手指运动的模式识别中,稳定性和识别率是两个主要问题,为此提出了一种新的EMG模式识别算法。该算法采用现代信号处理理论中的AR模型和改进的BP神经网络相结合的算法,有效的解决了BP网络识别中落入局部极值问题。进行试验,将提取到的特征值输入MATLAB建立一个改进多层BP神经网络,识别三个不同类型的手指运动。实验表明,改进BP算法较传统BP算法获得了更高的识别精度,达到94%左右。

The SVD of the EMG basis on the FFT of the Kaiser window

The EMG signal is a present field of research which is a driving force in sources of rehabilitating robots. The FFT with Kaiser Window was used in this paper to analyze the spectral characteristics of the EMG signal according to the characteristic of time changing and nonlinearity for the EMG signal and good results have been obtained. The singular value expressing the property of every EMG signal at each channel was taken out. It offered important data for the actual control of rehabilitating robots.

sEMG信号采集电路设计及其特征提取算法

表面肌电(Surface electromyography,sEMG)信号直接、客观地反映了神经和肌肉的活动功能状态,已获得广泛应用。本文设计了一种sEMG信号采集电路并以单通道形式采集上肢5种动作的sEMG信号,经小波包变换提取6种特征(其中一种引自基于小波变换的特征提取方法)并分别结合PCA和KPCA进行处理;再分别用BP神经网络和SVM进行动作识别。此外,对比了小波变换的特征提取;讨论了KPCA与PCA在特征变换上的差异。所提取的基于小波包变换的6种特征有5种的识别率均超过95.7%,其中引入的高低频系数组合特征在BP神经网络下平均识别率超过99%。基于小波变换提取的5种特征经KPCA变换后也达到较高的识别率。实验结果表明,本文的sEMG信号采集方法及其特征提取方法均达到较好效果。

面向八通道肌电信号处理的微控制器设计

该文提出了一种基于嵌入式ARM系统的面向肌电信号处理的微控制器设计方案。利用肌电信号采集和处理模块对人体表面肌电进行采集、滤波和放大处理,通过A/D转换器将处理后的数据传输到嵌入式ARM系统中,实现肌肉运动状态的实时监测。为了实现高速采集模拟信号并将其转换为数字信号,该文在硬件设计中采用了内置8通道的12位A/D转换器和转存A/D数据的DMA控制器于一体的nRF52840芯片解决方案。实验结果表明,设计的肌电微控制器具有高精度、高稳定性和快速响应的特点,能够满足基于表面肌电信号的手势识别或假手控制等研究的要求。