基于肌电信号的无线通讯控制系统

表面肌电信号是肌肉动作产生的一种生物电信号,其中蕴含了与人体动作相关的运动意图信息.因此,通过对肌电信号的分析处理,可以获取人体的动作模式.基于肌电信号的研究在康复医学,假肢控制,生物医疗等方面具有巨大的应用前景.研究基于表面肌电信号的分类识别构建一个无线通讯控制系统,研究内容包括表面肌电信号的实验设计,分析方法介绍,手势动作获取,无线通讯系统搭建及实时界面显示控制系统的构建.通过对整个系统的试验调试,该系统操作简单,具有良好的实时性.该课题的研究工作能够为辅助手臂装置或残障人士轮椅控制提供新的模式.     

基于相干性的多频段脑肌电信号双向耦合分析

针对皮层肌肉相干性分析时不能确定耦合方向的局限性,根据神经肌肉信息的双向传递性,提出利用不同大脑功能区的脑电信号和动作相关的肌电信号,实现了相干函数对脑肌电信号的双向耦合分析.本文对不同握力模式下同步采集的脑肌电信号进行了多频段耦合分析.通过下行(EEG—>EMG)和上行(EMG—>EEG)分析发现,随着握力的增大,EEG能量、相干幅值和耦合强度均向高频段转移.与基于新型格兰杰因果关系的耦合方法进行比较,验证了相干性方法进行皮层肌肉双向耦合分析的可行性和优势.研究结果为探索基于皮层肌肉相干性的双向手部运动信息解码和上肢运动功能障碍分析提供了依据.     

基于眼肌肌电信号分析的眨眼识别

通过提取与分析人体的肌电信号,能有效地获取生物动作信息,帮助瘫痪病人恢复某些特定的生物功能.对上眼提肌肉的肌电信号进行分析与研究,提出了眨眼行为判断识别装置的设计过程.采用优化算法以及使用相关模拟软件(SIMULINK[2])、电路模型和选取恰当的神经元,达到有效地减弱外界噪声对肌电信号的干扰,省去小波建模分析,并在实验中取得了高准确度的判断效果.     

一种改进的基于信号能量阈值的表面肌电信号自动分割方法

表面肌电信号中连续动作信号的有效分段提取是对信号分析和处理的前提,提出了一种改进的肌电信号自动分割方法,为实现康复机器人信号全自动分析奠定了基础。该方法将表面肌电信号窗口能量作为肌肉动作始末点的判决标准,给出初始阈值计算公式。同时结合小波变换技术对非动作信号进行滤波,并根据分割点特征提出分割阈值自动调节方法。实验表明,该方法可以自动分割肌电信号,无需考虑测试者自身因素的影响,无需手工设定初值,分割结果准确,精度较高。     

人体肌电信号的生理层模型

研究表面肌电信号模型,能够更精确地描述肌肉在活动时所产生的肌电信号的形成过程.因此表面肌电信号模型的建立,可以实现对神经肌肉的控制,以及肌电信号的产生等问题的理解,为其特定参数值的提取,以及信号的实用性和真实性的分析打下了基础.在考虑了影响人体肌电信号主要参数值的特征条件后,通过matlab仿真软件建立一个能够仿真运动单位在不同激励下的表面肌电信号.目前的生理层仿真模型参数不可调,使得模型的精准性较差,而上述的改进和优化实现了参数的可调控性,使得模型更具实用性.     

表面肌电信号的AR 参数模型分析方法

根据实际肌电信号的随机性特征，对其建立AR(Autoregressive)模型，得到其AR模型的各项参数，分析此系数和对应肌肉活动所确定的肢体动作之间的关系，从而得到基于动作模式的表面肌电信号(EMG)AR模型参数分析方法。     

基于肌电信号的行走步态周期识别方法与实验系统的设计

通过下肢表面肌电信号的人体行走步态周期识别方法设计了实验系统;针对表面肌电信号的微弱性、交变性、低频性等特点,提出了识别肌肉动作起始时刻的峰—谷线性插值分段积分算法,并将该算法与阈值法相结合,提取足跟着地前肌肉动作起始时刻,从而达到划分步态周期的目的;该方法仅需单通道信号作为信息源,不同被测者可以选用不同的肌电信号;有效回避了肌电信号传感器零点漂移现象;文中分别对5位被测者行走时的7个下肢肌电信号进行采集,以VisualC++为工具,基于用户界面设计了步态周期的识别系统,其系统识别结果验证了该方法具有广泛性、可靠性、准确性和实用性。     

痉挛对双瘫型脑瘫儿童下肢肌肉相关性的影响

为了探究双瘫脑性瘫痪儿童下肢肌肉相关性,并分析痉挛特性对双瘫患者下肢肌肉相关性的影响,文章采用表面肌电信号作为信息来源。对 12 例脑瘫患儿进行下肢腓肠肌和胫骨前肌肌电信号采集以及肌张力测试。信号经过 Acqknowledge 软件进行滤波处理,所得数据采用 SPSS 19.0 软件进行统计学分析。实验结果表明,痉挛特性减弱了双瘫患儿下肢肌肉相关性。该研究为脑瘫患儿的基础研究及康复训练提供了参考依据。     

基于MSP430的肌电假手系统设计

在对人体表面肌电信号研究的基础上,设计出一种肌电假手系统,其中包括肌电信号采集调理系统和假手控制系统。肌电信号经信号调理电路放大、滤波、陷波后,由低功耗的MSP430F149单片机进行A/D转换、特征计算。单片机结合肌电信号与触滑觉传感器反馈的信息来控制电机转向与转速,从而控制假手做出相应动作。通过实际采集的肌电信号在示波器上显示的波形与假手的动作进行对比,说明系统设计是合理有效的。     

推拿手法评估中表面肌电信号的应用探索

本文以推拿手法中的揉法为例,通过讨论揉法按摩专家实施揉法按摩时手臂表面肌电信号的特征规律,探索表面肌电信号在按摩手法评价中的应用。实验表明表面肌电信号可以实时记录推拿实施者手臂肌肉用力情况;能客观地评价实施者不同肌肉在操作过程中的施力情况,从而达到对推拿实施者的手法进行评价及校正的目的。     

Recognizing the breathing resistances of wearing respirators from respiratory and sEMG signals with artificial neural networks

This study is devoted to recognizing the breathing resistances of wearing respirators from respiratory and surface electromyography (sEMG) signals. Ten subjects were required to sit for 5 min and walk for 5 min while wearing two different models of N95 filtering facepiece respirators (FFRs) and without a respirator. We recorded the sEMG signals from the respiratory muscles of the subjects, and the respiratory amplitude is also collected. Subsequently, fifteen features of the sEMG time domain and respiratory amplitude were extracted and used as input vectors to a recognition model based on artificial neural networks (ANNs). Finally, the experimental results show that these artificial neural networks are effective for recognizing different airway resistances of wearing respirators from sEMG and respiratory signals. The results also indicate that abdominal and scalene are the primary respiratory muscles affected by using N95 FFRs.Relevance to industryRespirator manufactures and administrations can readily employ this paper's findings for recognizing the breathing resistances of wearing respirators from respiratory and surface electromyography (sEMG) signals based on artificial neural networks automatically. Observations of the present study are in support of testing only the two primary muscles (abdominal and scalene) to simplify the evaluation of the effects of the breathing resistances of wearing respirators on respiratory muscles.     

基于高密度肌电的对称位置发音肌肉对语音识别 贡献的研究

说话是人类正常生活中最重要的技能之一,是发音相关肌肉在神经中枢的控制下协调运动的 结果。表面肌电图法(Surface Electromyography,sEMG)是目前采集肌肉电信号的常用方法,能检测到 可靠的肌肉电生理信息。用肌电信号进行语音分类时,所选的电极位置对分类精度有重大作用。但目 前基于 sEMG 的语音识别方法选取电极位置及数量时没有一个客观的指标,也不清楚发音相关的面、 颈部左右两侧对称位置电极对肌电语音识别的贡献是否冗余。该文使用 120 通道电极(关于面中、颈 中对称)采集了 8 名发音正常的受试者分别发 5 个中文单词和 5 个英文单词时的面、颈部 sEMG,考察 了面、颈部左右两侧对称位置 sEMG 对语音识别的贡献。结果表明,发音过程中面、颈部左右两侧肌 肉活动有相似的变化规律,但整个活动过程中面部对称位置的相关性比颈部低;使用颈部左侧、右侧 的肌电信号进行语音分类的分类精度区别不大,而使用面部左、右两侧肌电信号的分类结果差异较明 显。因此,颈部对称位置的 sEMG 信号对语音识别贡献程度具有一致性,而面部则不具有,这为后续 研究减少电极数量和选择最佳通道提供了新思路。     

Nonlinear multiscale Maximal Lyapunov Exponent for accurate myoelectric signal classification

Surface Electromyography (sEMG) is a non-invasive, easy to record signal of superficial muscles from the skin surface. The sEMG is widely used in evaluating the functional status of the hand to assist in hand gesture recognition, prosthetics and rehabilitation applications. Considering the nonlinear and non-stationary characteristics of sEMG, hand gesture recognition using sEMG signals necessitate designers to use Maximal Lyapunov Exponent (MLE) or ensemble Empirical Mode Decomposition (EMD) based MLEs. In this research, we propose a hand gesture recognition method of sEMG based on nonlinear multiscale MLE. The aim is to increase the classification accuracy of sEMG features while reducing the complexity of EMD. The nonlinear MLE features are classified using Flexible Neural Tree (FNT), which can solve highly structured dependent problems of the Artificial Neural Network (ANN). The testing has been conducted using several experiments with five participants. The classification performance of nonlinear multiscale MLE method is compared with MLE and EMD-based MLE through simulations. Experimental results demonstrate that the former algorithm outperforms the two latter algorithms and can classify six different hand gestures up to 97.6% accuracy.     

Analysis of upper-limb muscle fatigue in the process of rotary handling

Workers engaged in repetitive manual material handling (MMH) generally suffer from work-related musculoskeletal disorders (WMSDs), particularly in the arms, shoulders, neck, and waist; this significantly limits the individual's strength and ability to work. Currently, research on upper-limb injuries affecting manufacturing workers focusses on the effect of different working conditions on specific muscle fatigue. However, research on the fatigue transformation relationship among various muscles in the process of working is lacking. Therefore, the purpose of this study was to investigate the fatigue changes between the upper-limb muscles during rotary handing. In this study, 13 male subjects were studied to simulate rotating handling during the manual handling process using surface electromyography (sEMG). Handling angles of 90°, 45°, and 0° were arranged as single variables to conduct the single-factor experiment. The sEMG of the brachioradialis, biceps brachii, trapezius, and multifidus muscle was measured during the operation. The results of this study indicate that the characteristics of muscle fatigue are different at different rotation angles: the multifidus muscle and trapezius were fatigued when the rotation was 90°. Under the condition of a 45° rotation, the activities of the four muscles fluctuated significantly. The slope of the median frequency of the muscles was positive, the load of the four muscles was evenly distributed, and no local fatigue was observed. Under the condition of a 0° rotation, the sEMG indicated that the brachioradialis muscle was fatigued, while the other three muscles had lower loads.     

基于LabVIEW的多通道sEMG信号检测系统设计

针对多通道信号检测系统在表面肌电信号sEMG(surface electromyography)信号检测分析中的应用,设计了一种基于LabVIEW的多通道sEMG信号检测系统。该系统由前置调理电路、数据接口卡以及LabVIEW软件编程部分组成。利用该系统采集并分析健康受试者完成指力跟踪动作时前臂指总伸肌上4通道sEMG信号时频域的特征值。实验结果表明,该系统能实现4通道sEMG信号的实时采集,并得到与手指力量相关的sEMG信号时域特征和频域特征,验证了所设计检测系统是可行的。     

基于柔性印刷工艺的表面肌电电极阵列装置的设计

设计了一种基于柔性印刷工艺的表面肌电电极阵列装置。该电极阵列由12个直径1.2mm的镀金圆电极分成两列组成,内部电极间距为3mm。电极载体材料(聚酰亚胺,厚50μm)具有较高的机械柔性,表面镀金(厚度2μm)的电极具有较低的阻抗,特制的聚酯双面胶带用于可重复使用的电极阵列装置的固定。在单指力量输出任务时记录指浅屈肌的多通道表面肌电(surface Electromyogram,sEMG)信号的实验中得到了稳定的基线和较好的sEMG信号。初步的实验结果表明,设计的这种低成本、体积小的高密度电极阵列装置能用于表面肌肉空间sEMG信号的检测。     

上肢经肱骨截肢神经功能重建研究

表面肌电信号是一种安全、非侵入的电生理信息,作为实现直觉控制多功能肌电假肢系统的信息源而被广泛应用。由于经肱骨截肢者截肢的程度较高,残留的肢体肌肉少,缺乏足够的肌电信息源,无法实现多功能肌电假肢的直觉控制。目前现有技术是通过采用靶向肌肉神经功能重建的方法重建缺失肌电信息源。但目前国内尚未有关于截肢者残端神经功能重建方法的相关研究。因此,文章提出一种新型的神经吻合技术——目标神经功能替代术：采用靶向肌肉神经功能重建术与目标神经功能替代术相结合的方法,首次在国内对经肱骨截肢者成功实施了神经功能重建手术,成功建立了经肱骨截肢者神经功能重建模型,重建了因截肢而丧失的肌电信息。并采用高密度肌电技术对术前和术后的手-腕-肘部动作进行肌电信号采集,通过动作分类识别的准确率验证了该手术后肌电信息源重建的可靠性。这些结果初步验证了该方法可以为经肱骨截肢者残肢重建缺失肢体神经功能,并为直觉控制多功能肌电假肢提供潜在的信息源。     

Feature extraction of surface electromyography signals with continuous wavelet entropy transform

A novel tool of bio signal processing is proposed to identify human muscle action through sEMG. The tool is based on Integration of continuous wavelet transforms, wavelet time entropy and wavelet frequency entropy to identify muscle actions through sEMG. The experiments are carried out on triceps, biceps and flexor digitorum superficial (FDS) muscles. sEMG signals are measured at different intensities of FDS muscle contractions in order to verify the consistency of results. By taking the average entropies and based on lowest average wavelet entropy, it is found in calibrated experiment that complex Shannon wavelet family is the best candidate to identify the muscle activities among: Derivative of Gaussians wavelet family, Derivative of Complex Gaussians wavelet family, Complex Morlet family, Symlets, Coiflets and Daubechies wavelet families. Moreover, the results are consistent over the time-variant signal. The results presented in this paper have futuristic engineering implication in biomedical engineering and bio-robotic applications.     

Effects of a novel neck balance system on neuromuscular fatigue of neck muscles during repeated flexions and extensions

This study aimed at investigating the effects of a novel neck balance system (NBS), which is a baseball cap with counterweights in the occipital part, on neuromuscular fatigue of neck muscles during and after a full‐range repeated neck flexion‐extension task. Surface electromyography (sEMG) of sternocleidomastoid (SCM) and semispinalis capitis (SC) muscles was recorded in 15 healthy individuals during full‐range flexion‐extension movements of the neck lasting 5 min at a fixed pace (1 Hz), with or without NBS. Maximal isometric force and sEMG were recorded before and after the fatiguing task. During the fatiguing task, the SC muscle showed a higher decline in amplitude of sEMG with NBS than without NBS, while no differences in the SCM muscle emerged between the two conditions. Maximal isometric force of both neck flexor and extensor muscles decreased significantly after the fatiguing task, both with NBS (p < .05) and without NBS (p < .05), with no differences between the two conditions. In conclusion, adopting the NBS promotes an increase of the activation of neck extensor muscles, possibly leading to earlier decline of the neuromuscular performance and to diminished ability to actively stabilize neck structures. For these reasons, the adoption of the NBS during dynamic, fatiguing contractions may not be appropriate.     

sEMG-based continuous estimation of joint angles of human legs by using BP neural network

In this paper, we propose an mth order nonlinear model to describe the relationship between the surface electromyography (sEMG) signals and the joint angles of human legs, in which a simple BP neural network is built for the model estimation. The inputs of the model are sEMG time series that have been processed, and the outputs of the model are the joint angles of hip, knee, and ankle. To validate the effectiveness of the BP neural network, six able-bodied people and four spinal cord injury (SCI) patients participated in the experiment. Two movement modes including the treadmill exercise and the leg extension exercise at different speeds and different loads were respectively conducted by the able-bodied individuals, and only the treadmill exercise was selected for the SCI patients. Seven channels of sEMG from seven human leg muscles were recorded and three joint angles including the hip joint, knee joint and the ankle joint were sampled simultaneously. The results present that this method has a good performance on joint angles estimation by using sEMG for both able-bodied subjects and SCI patients. The average angle estimation root-mean-square (rms) error for leg extension exercise is less than 9°, and the average rms error for treadmill exercise is less than 6° for all the able-bodied subjects. The average angle estimation rms error of the SCI patients is even smaller (less than 5°) than that of the able-bodied people because of a smaller movement range. This method would be used to rehabilitation robot or functional electrical stimulation (FES) for active rehabilitation of SCI patients or stroke patients based on sEMG signals.