Design of Real-Time Document Control Based on Zigbee and Surface Electromyography (sEMG)

The human-computer interaction (HCI) is now playing a great role in computer technology. This study introduces an automatic document control technique which is based on the human hand waving movements. The recognition of hand movement is realized according to the surface electromyography (sEMG). A collector is set on the forearm. The sEMG signal is recorded and conveyed to a PC terminal by using wireless Zigbee. An automatic algorithm is developed in order to extract the characteristics of sEMG, recognize the waving movements, and transmit to document control command. The developed human-computer interaction technique can be used as a new gallery for teaching, as well as an assistant tool for disabled person.

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。

基于步态事件和sEMG的功能性电刺激起始点研究

足下垂患者步行过程中进行功能性电刺激可以帮助其恢复正常行走能力,而准确确定功能性电刺激的开启时间至关重要。文章针对该问题,利用步行过程中下肢的角速度和表面肌电信号(surface electromyography,sEMG),提出一种以步态事件与肌肉动作点之间延时关系为控制策略的足下垂步行过程中功能性电刺激准确开启的方法。根据步态信息和sEMG电信号特征对大腿处的角速度数据进行步态事件划分,试验结果表明步态事件划分得具有良好一致性;利用模糊熵算法对去噪后的sEMG信号进行肌肉运动起始点T muscle的判定,确定T muscle与脚尖离地(toe off,TO)之间的延时时间关系;结合所划分的步态事件特征点,确定电刺激起始点T on。该文为足下垂治疗中功能性电刺激开启时间点的确定提供了一种新的辨识方法。

Pattern recognition of surface electromyography signal based on wavelet coefficient entropy

This paper introduced a novel, simple and ef-fective method to extract the general feature of two surface EMG (electromyography) signal patterns: forearm supination (FS) surface EMG signal and forearm pronation (FP) surface EMG signal. After surface EMG (SEMG) signal was decomposed to the fourth resolution level with wavelet packet transform (WPT), its whole scaling space (with frequencies in the interval (0Hz, 500Hz]) was divided into16 frequency bands (FB). Then wavelet coefficient entropy (WCE) of every FB was calculated and corre-spondingly marked with WCE(n) (from the nth FB, n=1,2,…16). Lastly, some WCE(n) were chosen to form WCE feature vector, which was used to distinguish FS surface EMG signals from FP surface EMG signals. The result showed that the WCE feather vector consisted of WCE(7) (187.25Hz, 218.75Hz) and WCE(8) (218.75Hz, 250Hz) can more effectively recog-nize FS and FP patterns than other WCE feature vector or the WPT feature vector which was gained by the combination of WPT and principal components analysis.

基于sEMG信号几何特征的肌肉疲劳分类

为了更好地区分肌肉疲劳程度,本文通过小波变换的方法,分析不同频段中表面肌电(sEMG)信号的能量变化情况,提取信号几何特征,对肌肉非疲劳和疲劳状态进行区分。从几何边界区域中提取周长、面积、圆度特征,分析几何特征变化情况。同时,使用分类器对肌肉疲劳进行分类。实验结果表明:几何特征对肌肉疲劳状态有更加直观的区分效果。几何特征在肌肉疲劳前后有明显变化,相比传统时域、频域特征,具有更好的分类效果,对几何特征进行特征融合,能够有效提升分类准确度。

Surface electromyography for diagnosing dysphagia in patients with cerebral palsy

AIM: To determine the accuracy of 2-channel surface electromyography(sE MG) for diagnosing oropharyngeal dysphagia(OPD) in patients with cerebral palsy.METHODS: Participants with cerebral palsy and OPD between 5 and 30 years of age and age- and sexmatched healthy individuals received s EMG testing during swallowing. Electrodes were placed over the submental and infrahyoid muscles, and s EMG recordings were made during stepwise(starting at 3 mL) determination of maximum swallowing volume. Outcome measures included submental muscle group maximum amplitude, infrahyoid muscle group maximum amplitude(IMGMA), time lag between the peak amplitudes of 2 muscle groups, and amplitude difference between the 2 muscle groups.RESULTS: A total of 20 participants with cerebral palsy and OPD(OPD group) and 60 age- and sex-matched healthy volunteers(control group) were recruited. Among 20 patients with OPD, 19 had Dysphagia Outcome and Severity Scale records. Of them, 8 were classified as severe dysphagia(level 1), 1 was moderate dysphagia(level 3), 4 were mild to moderate dysphagia(level 4), 3 were mild dysphagia(level 5), and 3 were within functional limits(level 6). Although the groups were matched for age and sex, participants in the OPD group were significantly shorter, weighed less and had lower body mass index than their counterparts in the control group(both, P < 0.001). All s EMG parameter values were significantly higher in the OPD group compared with the control group(P < 0.05). Differences were most pronounced at the 3 mL swallowing volume. IMGMA at the 3 mL volume was the best predictor of OPD with a sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of 85.0%, 90.0%, 73.9%, 94.7% and 88.8%, respectively.

Chaos, Fractal and Recurrence Quantification Analysis of Surface Electromyography in Muscular Dystrophy

We analyze muscular dystrophy recorded by sEMG and use standard methodologies and nonlinear chaotic methods here including the RQA. We reach sufficient evidence that the sEMG signal contains a large chaotic component. We have estimated the correlation dimension (fractal measure), the largest Lyapunov exponent, the LZ complexity and the %Rec and %Det of the RQA demonstrating that such indexes are able to detect the presence of repetitive hidden patterns in sEMG which, in turn, senses the level of MU synchronization within the muscle. The results give also an interesting methodological indication in the sense that it evidences the manner in which nonlinear methods and RQA must be arranged and applied in clinical routine in order to obtain results of clinical interest. We have studied the muscular dystrophy and evidence that the continuous regime of chaotic transitions that we have in muscular mechanisms may benefit in this pathology by the use of the NPT treatment that we have considered in detail in our previous publications.

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.

基于sEMG的拉力作业肌肉疲劳与恢复研究

目的为了探究动态拉力作业肌肉疲劳与恢复的特征,避免肌肉疲劳累积,降低肌肉骨骼疾患(MSDs)风险。方法设计动态拉力作业疲劳与恢复试验,选取10名男性本科生。测量屈指肌和肱三头肌的表面肌电信号,通过统计学方法分析肌群指标MF和MPF的变化特征。结果经过静坐恢复方式干预,肌力恢复至88%MVC,屈指和肱三头肌肌电频域指标MF和MPF均呈上升趋势,主观疲劳感随休息时间延长呈下降趋势。结论肌电频域指标MF和MPF能够较好地作为评估动态拉力作业中肌肉疲劳状态恢复过程的客观指标。本研究的疲劳状态恢复特征研究内容可为现实拉力作业中的休息设计提供依据。

Control method for exoskeleton ankle with surface electromyography signals

This paper is concerned with a control method for an exoskeleton ankle with electromyography （EMG） signals. The EMG signals of human ankle and the exoskeleton ankle are introduced. Then a control method is proposed to control the exoskeleton ankle using the EMG signals. The feed-forward neural network model applied here is composed of four layers and uses the back-propagation training algorithm. The output signals from neural network are processed by the wavelet transform. Finally the control orders generated from the output signals are passed to the motor controller and drive the exoskeleton to move. Through experiments, the equality of neural network prediction of ankle movement is evaluated by giving the correlation coefficient. It is shown from the experimental results that the proposed method can accurately control the movement of ankle joint.

Real-time surface electromyography in Parkinson's disease patients during exercise-induced muscle fatigue

To explore the mechanisms underlying exercise-induced local muscle fatigue in patients with idiopathic Parkinson＇s disease （PD）,we used surface electromyography to record myoelectric signals from the tibialis anterior muscle during isometric contraction-induced fatigue until exhaustion.The results revealed no significant differences between patients with idiopathic PD and healthy controls in maximum voluntary contraction of the tibialis anterior muscle.The basic characteristics of surface electromyography were also similar between the two groups.The duration of isometric contraction at 50% maximum voluntary contraction was shortened in PD patients.In addition,PD patients exhibited a stronger increase in mean square amplitude,but a weaker decrease in median frequency and mean power frequency compared with healthy controls during isometric contraction.The skeletal muscles of PD patients revealed specificity of surface electromyography findings,indicating increased fatigability compared with healthy controls.

Robot driving and arm gesture remote control using surface EMG with accelerometer signals

This paper proposes a method of remotely controlling robots with arm gestures using surface electromyography(EMG)and accelerometer sensors attached to the operator's wrists.The EMG and accelerometer sensors receive signals from the arm gestures of the operator and infer the corresponding movement to execute the command to control the robot.The movements of the robot include moving forward and backward and turning left and right.The forearm of the robot can be rotated up,down,left and right,and the robot can clench its fists.The accuracy is over 99% and movements can be controlled in real time.

Hand Gestures Recognition Based on One-Channel Surface EMG Signal

This paper presents an experiment using OPENBCI to collect data of two hand gestures and decoding the signal to distinguish gestures. The signal was extracted with three electrodes on the subiect’s forearm and transferred in one channel. After utilizing a Butterworth bandpass filter, we chose a novel way to detect gesture action segment. Instead of using moving average algorithm, which is based on the calculation of energy, We developed an algorithm based on the Hilbert transform to find a dynamic threshold and identified the action segment. Four features have been extracted from each activity section, generating feature vectors for classification. During the process of classification, we made a comparison between K-nearest-neighbors (KNN) and support vector machine (SVM), based on a relatively small amount of samples. Most common experiments are based on a large quantity of data to pursue a highly fitted model. But there are certain circumstances where we cannot obtain enough training data, so it makes the exploration of best method to do classification under small sample data imperative. Though KNN is known for its simplicity and practicability, it is a relatively time-consuming method. On the other hand, SVM has a better performance in terms of time requirement and recognition accuracy, due to its application of different Risk Minimization Principle. Experimental results show an average recognition rate for the SVM algorithm that is 1.25% higher than for KNN while SVM is 2.031 s shorter than that KNN.

Role of a wireless surface electromyography in dystonic gait in functional movement disorders: A case report

BACKGROUND Dystonic gait(DG) is one of clinical symptoms associated with functional dystonia in the functional movement disorders(FMDs). Dystonia is often initiated or worsened by voluntary action and associated with overflow muscle activation. There is no report for DG in FMDs caused by an abnormal pattern in the ankle muscle recruitment strategy during gait.CASE SUMMARY A 52-year-old male patient presented with persistent limping gait. When we requested him to do dorsiflexion and plantar flexion of his ankle in the standing and seating positions, we didn’t see any abnormality. However, we could see the DG during the gait. There were no evidences of common peroneal neuropathy and L5 radiculopathy in the electrodiagnostic study. Magnetic resonance imaging of the lumbar spine, lower leg, and brain had no definite finding. No specific finding was seen in the neurologic examination. For further evaluation, a wireless surface electromyography(EMG) was performed. During the gait, EMG amplitude of left medial and lateral gastrocnemius(GCM) muscles was larger than right medial and lateral GCM muscles. When we analyzed EMG signals for each muscle, there were EMG bursts of double-contraction in the left medial and lateral GCM muscles, while EMG analysis of right medial and lateral GCM muscles noted regular bursts of single contraction. We could find a cause of DG in FMDs.CONCLUSION We report an importance of a wireless surface EMG, in which other examination didn’t reveal the cause of DG in FMDs.

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.

腹直肌分离度、盆底肌力及盆底sEMG参数与初产妇产后压力性尿失禁的关系

目的分析腹直肌分离度、盆底肌力及盆底表面肌电图(s EMG)参数与初产妇产后压力性尿失禁(SUI)的关系,探讨初产妇产后发生SUI的影响因素,以及腹直肌分离度、盆底肌力及s EMG参数对初产妇产后发生SUI的预测价值。方法选取2022年2月—2023年2月在上海市第一妇婴保健院住院分娩的116例初产妇,根据SUI发生情况分为SUI组(35例)和非SUI组(81例),比较2组产妇腹直肌分离度、盆底肌力及s EMG参数;采用逐步Logistic回归分析初产妇产后SUI发生的影响因素;绘制受试者工作特征(ROC)曲线分析腹直肌分离度、盆底肌力及s EMG参数对产后SUI的预测价值。结果2组产妇年龄、孕周、孕前身体质量指数(BMI)、孕期增体质量、新生儿体质量、有无阴道手术助产比例比较,差异均无统计学意义(P>0.05);SUI组产妇顺产比例高于非SUI组,差异有统计学意义(P<0.05)。SUI组产妇腹直肌分离度大于非SUI组,盆底肌力小于非SUI组,s EMG快速收缩阶段最大值、紧张收缩阶段平均值、紧张收缩阶段变异性、耐力收缩阶段平均值、耐力收缩阶段变异性均小于非SUI组,差异均有统计学意义(P<0.05)。逐步Logistic回归分析结果显示,顺产、腹直肌分离度、盆底肌力、s EMG紧张收缩阶段变异性、耐力收缩阶段平均值均是初产妇产后发生SUI的影响因素(P<0.05)。ROC曲线分析结果显示,腹直肌分离度、盆底肌力、耐力收缩阶段平均值及三者联合检测预测初产妇产后发生SUI的ROC曲线下面积(AUC)分别为0.838、0.874、0.870、0.957(均P<0.05)。结论产后发生SUI的初产妇存在腹直肌分离、盆底肌力及s EMG参数下降,检测腹直肌分离度、盆底肌力及s EMG参数对初产妇产后SUI有一定预测价值。

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 forearms 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.

基于sEMG信号和BPNN算法的机械臂控制系统设计

为了解决市场康复假肢功能单一、使用效果极差和价格昂贵等缺点,提出一种基于表面肌电信号的机械手控制系统。该系统主要分为两部分:一部分是基于Cortex-M4系列的肌电信号采集、预处理、BP神经网络分类的信号处理系统;另一部分是基于Cortex-M3系列的机械手臂控制系统。信号处理系统发出控制命令无线传输到机械臂,控制6舵机自由度的机械臂,实现6个动作的展示。试验结果证明:该系统能够实现6个动作的自学习,成功率在80%以上,系统有一定的应用价值。

基于sEMG-JASA的脊柱手术器械操作肌肉疲劳度测评

基于时频联合分析法(JASA),开展基于表面肌电信号的脊柱手术器械上肢操作肌肉疲劳评估研究,完成脊柱手术器械优化前后操作舒适性的对比测评。共招募17名受试者分别采集其肱桡肌和肱二头肌的表面肌电信号,选取5种优化前后的手术器械进行数据对比,基于RMS和MF特征值计算相同任务下各组器械的操作疲劳时间占比。结果表明完成相同操作任务时,优化前手术器械的操作疲劳时间显著高于优化后器械(P<0.05);操作同一器械,肱桡肌和肱二头肌疲劳状态无显著性差异(P>0.05),这为手术器械的人因学设计及疲劳损伤防护等提供客观数据及参考。

一种基于sEMG信号多重分形的肌肉疲劳特征分析方法

针对由表面肌电信号(sEMG)非平稳、非线性、自相似性等复杂特性导致的肌肉疲劳估计不准的问题,提出一种基于sEMG信号多重分形降趋移动平均法(MFDMA)的肌肉疲劳特征分析方法。首先,利用MFDMA方法对采集的sEMG信号、洗牌信号和高斯白噪声信号进行非线性动力学分析;其次,利用MFDMA方法计算sEMG信号的多重分形谱宽度、Hurst指数变化差值、概率测度值和峰值奇异指数4种多重分形特征;最后,利用t-检验法分析肌肉疲劳与非疲劳状态下的多重分形特征的显著差异性。结果表明,MFDMA方法能够描述sEMG信号的多重分形行为,谱宽等多重分形特征在肌肉疲劳与非疲劳状态下具有显著性差异。所提方法能够可靠表征运动性肌肉疲劳,可为肌肉疲劳识别模型建构、康复医学研究提供特征参考。