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。     

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。     

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。     

基于定量分析方法的动作表面肌电信号分析

介绍了非线性数据处理方法递归图法(recurrence plots,RP)及其定量分析方法(recurrence quantifi-cation analysis,RQA),并利用RP和RQA研究了动作表面肌电信号。研究发现,表面肌电信号在不同动作模式下其所对应的RP图在结构上差异明显,通过计算两通道肌电信号的RQA指标递归率,发现不同动作信号的RQA指标递归率值具有不同的聚类分布。该方法为肌电信号的动作模式分类提供了一种新的思路。     

等长伸膝动作的运动单元放电特征分析

本研究基于表面肌电分解技术,分析伸膝动作中不同发力状态下大腿肌肉运动单元的解码准确性,并对比神经特征和肌电特征在肌肉激活程度估计中的效果. 12名大学生分别以2种发力速度和4种发力等级完成伸膝动作的等长收缩.实验同步采集受试者股内侧肌和股外侧肌处的高密度表面肌电信号和伸膝动作收缩力.基于卷积核补偿算法解码肌电信号得到运动单元动作电位,提取神经特征用于收缩力的互相关分析.结果发现,对于股内侧肌,2种任务及4种收缩力等级下平均解码得到(7±4)个运动单元,股外侧肌平均解码得到(9±5)个运动单元.它们的平均脉冲信噪比(pulse-to-noise ratio,PNR)为30.1 d B,对应解码准确率大于90%.股内侧肌的两种神经特征与力之间的平均相关性分别为(0.79±0.08)和(0.80±0.08),股外侧肌的两种神经特征与力之间的平均相关性分别为(0.85±0.05)和(0.85±0.06).综上可见,基于肌电分解技术可以准确识别不同发力状态下大腿肌肉的运动单元放电活动,并且运动单元放电频率与伸膝动作力高度相关,研究结果可用于运动康复、运动训练及人机接口等领域.     

肌电信号的特征分析方法及其应用

肌电信号是产生肌力的生物电信号来源,反映了神经-肌肉系统在进行随意性和非随意性活动时的生物电变化情况,它与神经肌肉活动密切相关.伴随着肌电信号特征分析方法的日臻完善,蕴含在信号内的神经、肌肉信息,越来越多地被人们所掌握,并被广泛地应用于临床医学、康复医学、体育科学、医学工程学以及基础研究等诸多领域.因而肌电信号具有重要的应用价值和学术价值.现本文主要针对肌电信号的特征分析方法(时域分析、频谱分析、时频分析等方法)以及肌电信号相关领域的应用情况作以综述.     

八道多功能生理记录仪微机系统的建立和应用——肌电信号的计算机处理

高效、准确、定量是现代肌电信号分析技术力求达到的理想境界。随着计算机科学和医学电信号处理技术的发展和日臻成熟,这一理想已可能成为现实。本实验利用模数转换器(A/D板)将八道多功能生理记录仪与计算机连接,并通过编制肌电信号处理软件,建立了肌电信号微机处理系统。该系统除能迅速、准确地对八道肌电信号同步进行积分和频谱处理外,还具有较强的图形和数据表格显示打印功能。初步应用表明,肌电信号微机处理系统的建立,对于提高临床诊断和基础研究水平具有重要意义。     

三自由度前臂假手的肌电控制系统

生物电应用于控制早在五十年代就有人研究,只是随着电子工业的发展才逐步走向实用阶段。对于假手来说,目前国际上已有商品化的单自由度假手出现,而多自由度假手正在许多国家的实验室里进行研究。虽然有些国家的假肢行业以为肌电信号不稳定、易受干扰,而不欢喜肌电控制。但是随着对肌电信号的深入研究和电子工业中大规模集成电路的发展,以及人们期望有更完善的假手,因而肌电控制的多自由度假手仍然成为人们竞相研究的对象。我们所研制的是肌电控制三自由度前臂假手,是在手腕关节部位实现手指的开闭,腕的伸屈和腕的内外旋。假手的肌电控制系统包括肌电信号源的选定、控制逻辑的组成、表面导引电极和肌电信号放大器及数字逻辑控制电路等部分。     

Techniques of EMG signal analysis: detection, processing, classification and applications

Electromyography (EMG) signals can be used for clinical/biomedical applications, Evolvable Hardware Chip (EHW) development, and modern human computer interaction. EMG signals acquired from muscles require advanced methods for detection, decomposition, processing, and classification. The purpose of this paper is to illustrate the various methodologies and algorithms for EMG signal analysis to provide efficient and effective ways of understanding the signal and its nature. We further point up some of the hardware implementations using EMG focusing on applications related to prosthetic hand control, grasp recognition, and human computer interaction. A comparison study is also given to show performance of various EMG signal analysis methods. This paper provides researchers a good understanding of EMG signal and its analysis procedures. This knowledge will help them develop more powerful, flexible, and efficient applications.     

An Improved SSA-Based Technique for EMG Removal from ECG

Background

The electrocardiogram (ECG) signals provide important information about the heart electrical activities in medical and diagnostic applications. This signal may be contaminated by different types of noises. One of the noise types which has a considerable overlap with the ECG signals in frequency domain is electromyogram (EMG). Among the exciting approaches for de-noising the ECG signals, those based on singular spectrum analysis (SSA) are popular.

Review on electromyography signal acquisition and processing

Electromyography (EMG) is a technique for recording biomedical electrical signals obtained from the neuromuscular activities. These signals are used to monitor medical abnormalities and activation levels, and also to analyze the biomechanics of any animal movements. In this article, we provide a short review of EMG signal acquisition and processing techniques. The average efficiency of capture of EMG signals with current technologies is around 70%. Once the signal is captured, signal processing algorithms then determine the recognition accuracy, with which signals are decoded for their corresponding purpose (e.g., moving robotic arm, speech recognition, gait analysis). The recognition accuracy can go as high as 99.8%. The accuracy with which the EMG signal is decoded has already crossed 99%, and with improvements in deep learning technology, there is a large scope for improvement in the design hardware that can efficiently capture EMG signals.     

Recognition of Handwriting from Electromyography

Handwriting – one of the most important developments in human culture – is also a methodological tool in several scientific disciplines, most importantly handwriting recognition methods, graphology and medical diagnostics. Previous studies have relied largely on the analyses of handwritten traces or kinematic analysis of handwriting; whereas electromyographic (EMG) signals associated with handwriting have received little attention. Here we show for the first time, a method in which EMG signals generated by hand and forearm muscles during handwriting activity are reliably translated into both algorithm-generated handwriting traces and font characters using decoding algorithms. Our results demonstrate the feasibility of recreating handwriting solely from EMG signals – the finding that can be utilized in computer peripherals and myoelectric prosthetic devices. Moreover, this approach may provide a rapid and sensitive method for diagnosing a variety of neurogenerative diseases before other symptoms become clear.     

Influence of amplitude cancellation on the accuracy of determining the onset of muscle activity from the surface electromyogram

The purpose of the study was to quantify the influence of amplitude cancellation on the accuracy of detecting the onset of muscle activity based on an analysis of simulated surface electromyographic (EMG) signals. EMG activity of a generic lower limb muscle was simulated during the stance phase of human gait. Surface EMG signals were generated with and without amplitude cancellation by summing simulated motor unit potentials either before (cancellation EMG) or after (no-cancellation EMG) the potentials had been rectified. The two sets of EMG signals were compared at forces of 30% and 80% of maximum voluntary contraction (MVC) and with various low-pass filter cut-off frequencies. Onset time was determined both visually and by an algorithm that identified when the mean amplitude of the signal within a sliding window exceeded a specified standard deviation (SD) above the baseline mean. Onset error was greater for the no-cancellation conditions when determined automatically and by visual inspection. However, the differences in onset error between the two cancellation conditions appear to be clinically insignificant. Therefore, amplitude cancellation does not appear to limit the ability to detect the onset of muscle activity from the surface EMG.     

An equilibrium-point model of electromyographic patterns during single-joint movements based on experimentally reconstructed control signals

The purpose of this work has been to develop a model of electromyographic (EMG) patterns during single-joint movements based on a version of the equilibrium-point hypothesis, a method for experimental reconstruction of the joint compliant characteristics, the dual-strategy hypothesis, and a kinematic model of movement trajectory. EMG patterns are considered emergent properties of hypothetical control patterns that are equally affected by the control signals and peripheral feedback reflecting actual movement trajectory. A computer model generated the EMG patterns based on simulated movement kinematics and hypothetical control signals derived from the reconstructed joint compliant characteristics. The model predictions have been compared to published recordings of movement kinematics and EMG patterns in a variety of movement conditions, including movements over different distances, at different speeds, against different-known inertial loads, and in conditions of possible unexpected decrease in the inertial load. Changes in task parameters within the model led to simulated EMG patterns qualitatively similar to the experimentally recorded EMG patterns. The model's predictive power compares it favourably to the existing models of the EMG patterns.     

Sample entropy analysis of surface EMG for improved muscle activity onset detection against spurious background spikes

Voluntary surface electromyogram (EMG) signal is sometimes contaminated by spurious background spikes of both physiological and extrinsic or accidental origins. A novel method of muscle activity onset detection against such spurious spikes was proposed in this study based primarily on the sample entropy (SampEn) analysis of the surface EMG. The method takes advantage of the nonlinear properties of the SampEn analysis to distinguish voluntary surface EMG signals from spurious background spikes in the complexity domain. To facilitate muscle activity onset detection, the SampEn analysis of surface EMG was first performed to highlight voluntary EMG activity while suppressing spurious background spikes. Then, a SampEn threshold was optimized for muscle activity onset detection. The performance of the proposed method was examined using both semi-synthetic and experimental surface EMG signals. The SampEn based methods effectively reduced the detection error induced by spurious background spikes and achieved improved performance over the methods relying on conventional amplitude thresholding or its extended version in the Teager Kaiser Energy domain.     

Estimation of monopolar signals from sphincter muscles and removal of common mode interference

The detection of surface electromyogram (EMG) by multi-electrode systems is applied in many research studies. The signal is usually recorded by means of spatial filters (linear combination of the potential under at least two electrodes) with vanishing sum of weights. Nevertheless, more information could be extracted from monopolar signals measured with respect to a reference electrode away from the muscle. Under certain conditions, surface EMG signal along a curve parallel to the fibre path has zero mean (property approximately satisfied when EMG is sampled by an array of electrodes that covers the entire support of the signal in space). This property allows estimating monopolar from single differential (SD) signals by pseudoinversion of the matrix relating monopolar to SD signals. The method applies to EMG signals from the external anal sphincter muscle, recorded using a specific cylindrical probe with an array of electrodes located along the circular path of the fibres. The performance of the algorithm for the estimation of monopolar from SD signals is tested on simulated signals. The estimation error of monopolar signals decreases by increasing the number of channels. Using at least 12 electrodes, the estimation error is negligible. The method applies to single fibre action potentials, single motor unit action potentials, and interference signals.The same method can also be applied to reduce common mode interference from SD signals from muscles with rectilinear fibres. In this case, the last SD channel defined as the difference between the potentials of the last and the first electrodes must be recorded, so that the sum of all the SD signals vanishes. The SD signals estimated from the double differential signals by pseudoinvertion are free of common mode.     

A Supplementary System for a Brain-Machine Interface Based on Jaw Artifacts for the Bidimensional Control of a Robotic Arm

Non-invasive Brain-Machine Interfaces (BMIs) are being used more and more these days to design systems focused on helping people with motor disabilities. Spontaneous BMIs translate user''s brain signals into commands to control devices. On these systems, by and large, 2 different mental tasks can be detected with enough accuracy. However, a large training time is required and the system needs to be adjusted on each session. This paper presents a supplementary system that employs BMI sensors, allowing the use of 2 systems (the BMI system and the supplementary system) with the same data acquisition device. This supplementary system is designed to control a robotic arm in two dimensions using electromyographical (EMG) signals extracted from the electroencephalographical (EEG) recordings. These signals are voluntarily produced by users clenching their jaws. EEG signals (with EMG contributions) were registered and analyzed to obtain the electrodes and the range of frequencies which provide the best classification results for 5 different clenching tasks. A training stage, based on the 2-dimensional control of a cursor, was designed and used by the volunteers to get used to this control. Afterwards, the control was extrapolated to a robotic arm in a 2-dimensional workspace. Although the training performed by volunteers requires 70 minutes, the final results suggest that in a shorter period of time (45 min), users should be able to control the robotic arm in 2 dimensions with their jaws. The designed system is compared with a similar 2-dimensional system based on spontaneous BMIs, and our system shows faster and more accurate performance. This is due to the nature of the control signals. Brain potentials are much more difficult to control than the electromyographical signals produced by jaw clenches. Additionally, the presented system also shows an improvement in the results compared with an electrooculographic system in a similar environment.