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。

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.

基于sEMG的多自由度下肢外骨骼康复机器人结构与控制策略

提出一种多自由度下肢外骨骼康复机器人,能够实现下肢多种康复训练。建立踝关节部分约束方程,采用数值法证明机构存在正确解。利用Delsys设备提取脚部运动的sEMG信号,采用LDA、RNN结合LSTM、CNN 3种信号分类方法,提出降维CNN方法,对输入运动进行识别和分类。最后,针对踝关节部分进行sEMG信号作为输出指令的脚部动作反馈实验,验证了该部分机构和控制方法的可行性和合理可靠性。

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.

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。

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.

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.

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。

Multifractal analysis of surface EMG signals for assessing muscle fatigue during static contractions

This study is aimed at assessing muscle fatigue during a static contraction using multifractal analysis and found that the surface electromyographic （SEMG） signals characterized multiffactality during a static contraction. By applying the method of direct determination ofthef（a） singularity spectrum, the area of the multifractal spectrum of the SEMG signals was computed. The results showed that the spectrum area significantly increased during muscle fatigue. Therefore the area could be used as an assessor of muscle fatigue. Compared with the median frequency （MDF）--the most popular indicator of muscle fatigue, the spectrum area presented here showed higher sensitivity during a static contraction. So the singularity spectrum area is considered to be a more effective indicator than the MDF for estimating muscle fatigue.

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IMU与sEMG混合信号的实时手势分类算法研究

为了提高表面肌电信号(surface electromyography,sEMG)的手势分类准确率,通过惯性测量单元(inertial measurement unit,IMU)与采集姿态信号与sEMG的混合信号,提出了GRUBiLSTM双层网络的实时手势分类算法。第1层门控循环单元(gated recurrent unit,GRU)利用能量组合算子特征对混合信号进行突变点检测,定位运动态数据起始点;第2层双向长短时记忆循环神经网络(Bi-directional long short term memory,BiLSTM)使用能量核相图特征对运动态混合信号进行2个方向10种手势的分类。通过离线模型优化,分类算法识别时间低于40 ms,突变点检测精度88.7%以上,手势分类准确率为85%,信息传输率(informationtranslaterate, ITR)达到89.9 bits/min,与基于机器学习的分类算法相比,在准确率与计算效率上具有优势。

基于ISSA-VMD和二代小波的sEMG信号降噪研究

表面肌电(sEMG)信号是一种可以有效表征肌肉活动的弱生理信号,采集过程中易受到多种噪声干扰。为解决变分模态分解(VMD)参数经验设置的问题,并进一步消除sEMG信号中的噪声,提出了一种基于改进麻雀算法(ISSA)优化VMD和二代小波阈值法相结合的sEMG信号降噪法。首先,采用基于改进T混沌映射、自适应权重和麻雀数目动态变化的改进麻雀算法并将品质因子作为目标函数对VMD进行参数寻优,然后利用ISSA优化的VMD分解对预处理过的sEMG信号进行分解,通过谱相关分析区分信号分量和噪声分量,最后对信号分量进行二代小波阈值法降噪,得到降噪信号。结果表明:ISSA较SSA有效提高了VMD参数寻优能力;在不同噪声等级下,基于ISSA-VMD和二代小波硬阈值的降噪法的降噪性能优于二代小波和ISSA-VMD;基于ISSA-VMD与二代小波硬阈值降噪法处理实际sEMG信号,能有效去除噪声。

基于sEMG和变刚度控制的虚拟假手交互系统

为使虚拟假手在交互时具有柔顺性,实现了一种基于表面肌电(sEMG)信号和变刚度控制的虚拟假手交互系统。首先,采集人体前臂的sEMG信号并从中估计人手的刚度水平和关节扭矩;然后,通过变刚度阻抗控制模型估计虚拟假手的关节角度;最后,使用估计的关节角度控制虚拟假手与虚拟环境中的物体进行交互,交互过程中根据虚拟假手与物体的交互力对关节角度进行动态调节。实验结果表明:基于sEMG和变刚度控制的虚拟假手在进行抓握物体的交互任务时能在成功抓握物体的前提下产生较小的交互力,从而实现柔顺的交互。

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。

不同高度负跟鞋行走时腹背肌群及下肢肌群sEMG特征分析

采用横断面研究,选取健康人45例当受试者,让受试者分别穿上平底鞋及4种不同负跟高度的鞋子,测量其直线匀速行走时的双侧竖脊肌、腹直肌、胫骨前肌、腓肠肌内侧肌的均方根值,观察行走时5种鞋型下上述8块肌群的均方根值,并采用广义估计方程进行进一步分析。观察健康人穿平底鞋及负跟0.5、1.0、1.5、2.0 cm的鞋子对行走时双侧腰椎竖脊肌、腹直肌、胫骨前肌、腓肠肌内侧肌的均方根值,并对其进行进一步分析。结果表明双侧腹直肌、腰椎旁竖脊肌的均方根值随负跟高度的增加无明显规律性,双侧胫骨前肌的均方根值在负跟1.0 cm处有低峰,余负跟高度呈递增趋势,右侧腓肠肌内侧头的均方根值随着负跟高度增加呈递增趋势,左侧腓肠肌内侧头在负跟1.5 cm之前呈递增趋势,在负跟2.0 cm处呈下降趋势。因此得出,随着负跟高度的增加,对腹直肌、竖脊肌的影响无明显趋势变化;随着负跟高度的增加,对胫骨前肌、腓肠肌内侧头的影响出现趋势变化,其中对腓肠肌内侧头的影响更大,但均方根值并非随着的负跟高度的增加呈单调递增。

A Hybrid Model Based on ResNet and GCN for sEMG-Based Gesture Recognition

The surface electromyography(sEMG)is one of the basic processing techniques to the gesture recognition because of its inherent advantages of easy collection and non-invasion.However,limited by feature extraction and classifier selection,the adaptability and accuracy of the conventional machine learning still need to promote with the increase of the input dimension and the number of output classifications.Moreover,due to the different characteristics of sEMG data and image data,the conventional convolutional neural network(CNN)have yet to fit sEMG signals.In this paper,a novel hybrid model combining CNN with the graph convolutional network(GCN)was constructed to improve the performance of the gesture recognition.Based on the characteristics of sEMG signal,GCN was introduced into the model through a joint voting network to extract the muscle synergy feature of the sEMG signal.Such strategy optimizes the structure and convolution kernel parameters of the residual network(ResNet)with the classification accuracy on the NinaPro DBl up to 90.07%.The experimental results and comparisons confirm the superiority of the proposed hybrid model for gesture recognition from the sEMG signals.

融合表面肌电和姿势信息的轮椅绩效评价方法

目的为客观评价轮椅的使用效益,使用表面肌电设备测试轮椅使用过程中的肌电信号,并融合姿势信号IMU来构建人机评价模型。方法分别对轮椅的折叠、刹车方式及行驶坡度进行试验来评估轮椅的使用绩效,通过对比用力肌群间的疲劳状况来判定较为舒适的轮椅折叠及刹车方式。实验要求被试者在执行轮椅任务时,分别使用两种折叠方式和三种刹车方式不同的轮椅进行实验,并在操作动作任务过程中采集sEMG和IMU信号,在实验任务结束后填写NASA-TLX量表。结果根据模型的评价指标对实验数据进行了比较与分析,横向收折式折叠和凹口式刹车(手刹位于前方)的轮椅疲劳度低,在3~4°坡度范围下轮椅使用者的受力最小,较为舒适,从而验证了模型在轮椅人机评价上的可行性,为优化轮椅设计提供参考。结论人机评价模型适用于评估产品绩效,同时提出的融合表面肌电和姿势信息的轮椅绩效评价方法具有较高的精度和准确性,能够有效地评估轮椅使用者的绩效水平。