RQA在肌电分析中的应用

介绍了递归图的生成方法和定量递归分析（RQA）中递归点的百发数,确定性线段的百分数,线段分布香农熵等分析量的意义。应用RQA分析肱二头肌及肱桡肌在不同负重下的肌电信号,发现肽二头肌肌电信号的递归点百分数的比肱肌高,有较强的周期性嵌入。与同一信号所作的FFT谱分析相比较,RQA法有较同的区分灵敏性,是肌电分析的一种新方法,它在其他复杂的生理信号处理中也有十分广阔的应用前景。     

不同负荷方式引起的腰部肌肉表面肌电信号变化特征

采用时频、复杂度和定量递归信号分析方法对Biering-sorensen和Ito-Shirado条件下腰部肌肉表面肌电信号的变化规律和特点进行了比较。10名正常受试者分别参加Biering-sorensen和Ito-Shirado运动负荷试验,分别获取两侧L2～L3和L5～S1部位表面肌电信号。研究发现,以上两种运动负荷方式下平均功率频率和复杂度时间序列曲线呈单调递减型变化,而确定性线段百分数时间序列曲线呈单调递增型变化。双因素方差分析表明负荷方式和采样部位对以上信号分析指标变化斜率的影响均有显著统计学意义,证明Biering-sorensen负荷方式较Ito-Shirado负荷方式对腰部肌肉表面肌电信号特征有较大的影响,而采样部位也是腰部肌肉功能评价不可忽视的因素。     

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。     

基于复杂性度量的表面肌电信号分类方法

提取表面肌电信号的复杂性测度信息,利用原始数据的复杂度指标构造特征矢量对四种前臂动作进行分类,取得了较好的识别效果.通过比较,发现基于原始数据的复杂度指标在分类性能上要优于基于重构序列的复杂度.肌电信号的复杂度算法简单,适合短数据运算,能够满足实时处理的要求.作为一种新的肌电信号特征,复杂性测度也为生理与病理分析提供了新的思路.     

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。     

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。     

肌肉动态收缩期间表面肌电信号的时频分析

介绍了用于肌肉动态收缩期间非平稳表面肌电信号的时频分析方法。用短时傅里叶变换、Wigner-Ville分布及Choi-Williams分布计算了表面肌电信号的时频分布,用于信号频率内容随时间演化的可视化观察。通过计算瞬时频谱参数,对肌肉疲劳的电表现进行量化描述。分析了反复性的膝关节弯曲和伸展运动期间从股外侧肌所记录的表面肌电信号。发现和在静态收缩过程中观察到的平均频率线性下降不同,在动态收缩期间瞬时平均频率的变化过程是非线性的并且更为复杂,且与运动的生物力学条件有关。研究表明将时频分析技术应用于动态收缩期间的表面肌电信号可以增加用传统的频谱分析技术不能得到的信息。     

表面肌电信号的肌肉疲劳判别研究进展

肌肉在周期的收缩或静态的拉伸过程中,会渐渐进入疲劳状态,肌肉疲劳特性的研究在康复医学、运动医学领域具有重要作用。表面肌电信号是从肌肉表面通过电极记录下来的反映神经肌肉系统活动的一维时间序列非平稳生物电信号,是评价局部肌肉疲劳的有效工具。本研究从时域和频域、时频域线性方法下的测量指标和非线性方法下的指标来综述表面肌电信号的疲劳研究进展,同时比较各种方法的优缺点,并对使用表面肌电信号来判别疲劳研究做了进一步的展望。     

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.     

基于表面肌电信号的人体动作识别算法研究进展

表面肌电信号(Surface Electromyography,sEMG)是通过相应肌群表面的传感器记录下来的一维时间序列非平稳生物电信号,不但反映了神经肌肉系统活动,对于反映相应动作肢体活动信息同样重要。而模式识别是肌电应用领域的基础和关键。为了在应用基于表面肌电信号模式识别中选取合适算法,本文拟对基于表面肌电信号的人体动作识别算法进行回顾分析,主要包括模糊模式识别算法、线性判别分析算法、人工神经网络算法和支持向量机算法。模糊模式识别能自适应提取模糊规则,对初始化规则不敏感,适合处理s EMG这样具有严格不重复的生物电信号;线性判别分析对数据进行降维,计算简单,但不适合大数据;人工神经网络可以同时描述训练样本输入输出的线性关系和非线性映射关系,可以解决复杂的分类问题,学习能力强;支持向量机处理小样本、非线性的高维数据优势明显,计算速度快。比较各方法的优缺点,为今后处理此类问题模式识别算法选取提供了参考和依据。     

The effect of muscle loading on the kinematics of in vitro glenohumeral abduction

This in vitro study evaluated the effects of four different muscle-loading ratios on active glenohumeral joint abduction. Eight cadaveric shoulders were tested using a shoulder simulator designed to reproduce unconstrained abduction of the humerus via computer-controlled pneumatic actuation. Forces were applied to cables that were sutured to tendons or fixed to bone, to simulate loading of the supraspinatus, subscapularis, infraspinatus/teres minor, and anterior, middle, and posterior deltoid muscles. Four sets of muscle-loading ratios were employed, based on: (1) equal loads, (2) average physiological cross-sectional areas (pCSAs), (3) constant values of the product of electromyographic (EMG) data and pCSAs, and (4) variable ratios of the EMG and pCSA data which changed as a function of abduction angle. The investigator generated passive motions with no muscle loads simulated. Repeatability was quantified by five successive trials of the passive and simulated active motions. There was improved repeatability in the simulated active motions versus passive motions, significant for abduction angles less than 40 degrees (p=0.02). No difference was found in the repeatability of the four different muscle-loading ratios for simulated active motions (p0.067 for all angles). The improved repeatability of active over passive motion suggests simulated active motion should be employed for in vitro simulations of shoulder motion.     

Abdominal muscle activation changes if the purpose is to control pelvis motion or thorax motion

The aim of this study was to compare trunk muscular recruitment and lumbar spine kinematics when motion was constrained to either the thorax or the pelvis. Nine healthy women performed four upright standing planar movements (rotations, anterior–posterior translations, medial–lateral translations, and horizontal circles) while constraining pelvis motion and moving the thorax or moving the pelvis while minimizing thorax motion, and four isometric trunk exercises (conventional curl-up, reverse curl-up, cross curl-up, and reverse cross curl-up). Surface EMG (upper and lower rectus abdominis, lateral and medial aspects of external oblique, internal oblique, and latissimus dorsi) and 3D lumbar displacements were recorded. Pelvis movements produced higher EMG amplitudes of the oblique abdominals than thorax motions in most trials, and larger lumbar displacements in the medial–lateral translations and horizontal circles. Conversely, thorax movements produced larger rotational lumbar displacement than pelvis motions during rotations and higher EMG amplitudes for latissimus dorsi during rotations and anterior–posterior translations and for lower rectus abdominis during the crossed curl-ups. Thus, different neuromuscular compartments appear when the objective changes from pelvis to thorax motion. This would suggest that both movement patterns should be considered when planning spine stabilization programs, to optimize exercises for the movement and muscle activations desired.     

Loop motions of triosephosphate isomerase observed with elastic networks

The internal dynamics of triosephosphate isomerase have been investigated with elastic networks, with and without a substrate bound. The slowest modes of motion involve large domain motions but also a loop motion that conforms to the changes observed between the crystal structures and . Our computations confirm that the different motions of this loop are important in several of the computed slowest modes. We have shown that elastic network computations on this protein system can combine atoms for the functional parts of the structure with coarse-grained (cg) representations of the remainder of the structure in several different ways. Similar loop motions are seen with elastic network models for atomistic and mixed cg models. The loop motions are reproduced with an overlap of 0.75-0.79 by combining the four slowest modes of motion for the free and complex forms of the enzyme.     

A motion of forearm supination with maintenance of elbow flexion produced by electrical stimulation to two elbow flexors in humans.

Motions of the forearm induced by electrical stimulation to two elbow flexors (brachioradialis: BR, biceps brachii: BB) were examined in five healthy human subjects. Stainless steel wire electrodes were implanted percutaneously into each motor point of the muscles. The muscles were stimulated separately with a computer-controlled multi-channel stimulator. The motions were taken with a digital video system. Angular changes of the motions in elbow flexion/extension and forearm pronation/supination were measured. Electromyograms (EMG) of BR, BB, and the triceps brachii (TB) were recorded. Electrical stimulation to BR induced a motion of flexion and that to BB motions of flexion and supination. The stimulation to BR with an adequate intensity provided holding of flexion with the prone forearm in all the subjects. In this situation, additional stimulation to BB resulted in motions of flexion and supination. However, the additional stimulation accompanied with a decrease of the stimulation intensity for BR provided a motion of supination with maintenance of the flexion in all the subjects. Since during the stimulation BR, BB, and TB showed no voluntary contraction in EMG, it is suggested that modulation of contraction between BR and BB by the stimulation can produce force in supination with keeping constant force in flexion to support the weight below the elbow.     

Sex-specific visual performance: female lizards outperform males in motion detection

In animal communication, complex displays usually have multiple functions and, male and female receivers often differ in their utilization and response to different aspects of these displays. The perceptual variability hypothesis suggests that different aspects of complex signals differ in their ability to be detected and processed by different receivers. Here, we tested whether receiver male and female Sceloporus graciosus lizards differ in visual motion detection by measuring the latency to the visual grasp response to a motion stimulus. We demonstrate that in lizards that largely exhibit complex motions as courtship signals, female lizards are faster than males at visually detecting motion. These results highlight that differential signal utilization by the sexes may be driven by variability in the capacity to detect different display properties.     

Estimation of dynamic joint torques and trajectory formation from surface electromyography signals using a neural network model

In this study, human arm movement was re-constructed from electromyography (EMG) signals using a forward dynamics model acquired by an artificial neural network within a modular architecture. Dynamic joint torques at the elbow and shoulder were estimated for movements in the horizontal plane from the surface EMG signals of 10 flexor and extensor muscles. Using only the initial conditions of the arm and the EMG time course as input, the network reliably reconstructed a variety of movement trajectories. The results demonstrate that posture maintenance and multijoint movements, entailing complex via-point specification and co-contraction of muscles, can be accurately computed from multiple surface EMG signals. In addition to the model's empirical uses, such as calculation of arm stiffness during motion, it allows evaluation of hypothesized computational mechanisms of the central nervous system such as virtual trajectory control and optimal trajectory planning.     

Feasibility of using EMG driven neuromusculoskeletal model for prediction of dynamic movement of the elbow.

Neuromusculoskeletal (NMS) modeling is a valuable tool in orthopaedic biomechanics and motor control research. To evaluate the feasibility of using electromyographic (EMG) signals with NMS modeling to estimate individual muscle force during dynamic movement, an EMG driven NMS model of the elbow was developed. The model incorporates dynamical equation of motion of the forearm, musculoskeletal geometry and musculotendon modeling of four prime elbow flexors and three prime elbow extensors. It was first calibrated to two normal subjects by determining the subject-specific musculotendon parameters using computational optimization to minimize the root mean square difference between the predicted and measured maximum isometric flexion and extension torque at nine elbow positions (0-120 degrees of flexion with an increment of 15 degrees ). Once calibrated, the model was used to predict the elbow joint trajectories for three flexion/extension tasks by processing the EMG signals picked up by both surface and fine electrodes using two different EMG-to-activation processing schemes reported in the literature without involving any trajectory fitting procedures. It appeared that both schemes interpreted the EMG somewhat consistently but their prediction accuracy varied among testing protocols. In general, the model succeeded in predicting the elbow flexion trajectory in the moderate loading condition but over-drove the flexion trajectory under unloaded condition. The predicted trajectories of the elbow extension were noted to be continuous but the general shape did not fit very well with the measured one. Estimation of muscle activation based on EMG was believed to be the major source of uncertainty within the EMG driven model. It was especially so apparently when fine wire EMG signal is involved primarily. In spite of such limitation, we demonstrated the potential of using EMG driven neuromusculoskeletal modeling for non-invasive prediction of individual muscle forces during dynamic movement under certain conditions.     

Analysis and classification of compressed EMG signals by wavelet transform via alternative neural networks algorithms

We propose intelligent methods for classifying three different muscle types, i.e. biceps, frontallis and abductor pollicis brevis muscles, with low computational complexity. For this aim, electromyogram (EMG) signals are recorded and modelled by using an auto-regressive (AR) model. As the size of the EMG signals is usually large, the computational complexity of artificial neural network (ANN) systems drastically increases. Therefore, in the proposed scheme EMG signals are pre-processed by using a wavelet transform and then they are modelled by employing an AR approach. The AR coefficients are used to train and test the ANNs. Experimental results show that the highest achieved classification accuracy is more than 95% in the case of EMG signals pre-processed by wavelet transform. The wavelet transform-based pre-processing significantly increases the performance rates compared to standard multilayer perceptron and general regression neural networks algorithms.