Research on the driver fatigue early warning model of electric vehicles based on the fusion of EMG and ECG signals

Electric vehicles have been rapidly developing worldwide due to the use of new energy.However,at the same time,serious traffic accidents caused by driver fatigue in emergency situations have also drawn widespread attention.The lack of datasets in real vehicle test environments has always been a bottleneck in the research of driver fatigue in electric vehicles.Therefore,this study establishes a dataset from real vehicle test,applies the Bayesian optimization support vector machine(BOA-SVM)algorithm to take features of electromyography(EMG)and electrocardiography(ECG)signals as input and develop an early warning model for driving fatigue detection.Firstly,the driver’s EMG and ECG signals are collected through real vehicle testing experiments and then combined with the driver’s subjective fatigue evaluation scores to establish the dataset.Secondly,the study establishes a driver fatigue early warning model for emergency situations.Time-domain and frequency-domain features are extracted from the EMG signals.Principal component analysis(PCA)is applied for dimensionality reduction of these features.The experimental results show that based on the input of dimensionality reduced EMG features and ECG features,the BOA-SVM algorithm achieved an accuracy of 94.4%in classification.

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。

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.

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.

Classification of uterine EMG signals using supervised classification method

Aim: The main purpose of this article is to detect any risk of preterm deliveries at an early gestation period using uterine electromyography signals. Detecting such uterine signals can yield a promising approach to determine and take actions to prevent this potential risk. Methods: The best position for the detection of different uterine signals is the median vertical axis of the abdomen. These signals differ from each other by their frequency content. Initially, simulation is done for the real detected EMG signals: preterm deliveries (PD) EMGs and deliveries at term (DT) EMGs. This is performed by applying autoregressive model (AR) of specific order to estimate AR coefficients of these real EMG signals. Finally, after calculation of the AR parameters of the two types of deliveries, we generate two types of simulated uterine contractions by using White Gaussian Noise (WGN). Frequency parameter extraction and classification are first applied on simulated signals to test the limits and performance of the used methods. The last remaining step is the classification of the contractions using supervised classification method. Results: Results show that uterine contractions may be classified using the Artificial Neural Networks (ANNs). The Simple Perceptron ANN is applied on the signals for their supervised classification into independent groups: preterm deliveries (PD) and deliveries at term (TD) according to their frequency content.

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.

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.

Feature Layer Fusion of Linear Features and Empirical Mode Decomposition of Human EMG Signal

To explore the influence of the fusion of different features on recognition,this paper took the electromyography(EMG)signals of rectus femoris under different motions(walk,step,ramp,squat,and sitting)as samples,linear features(time-domain features(variance(VAR)and root mean square(RMS)),frequency-domain features(mean frequency(MF)and mean power frequency(MPF)),and nonlinear features(empirical mode decomposition(EMD))of the samples were extracted.Two feature fusion algorithms,the series splicing method and complex vector method,were designed,which were verified by a double hidden layer(BP)error back propagation neural network.Results show that with the increase of the types and complexity of feature fusions,the recognition rate of the EMG signal to actions is gradually improved.When the EMG signal is used in the series splicing method,the recognition rate of time-domain+frequency-domain+empirical mode decomposition(TD+FD+EMD)splicing is the highest,and the average recognition rate is 92.32%.And this rate is raised to 96.1%by using the complex vector method,and the variance of the BP system is also reduced.

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.

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.

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。

Finger Flexion Motion Inference from sEMG Signals

This paper provides a method to infer finger flexing motions using a 4-channel surface Electronyogram （sEMG）. Surface EMGs are hannless to the humnan body and easily done. However, they do not reflect the activity of specific nerves or muscles, unlike invasive EMCs. On the other hand, the non-invasive type is difficult to use for discriminating various motions while using only a small number of electrodes. Surface EMG data in this study were obtained from four electodes placed around the forearm. The motions were the flexion of each 5 single fingers （thumb, index finger, middle finger, ring finger, and little fingers）. One subject was trained with these motions and another left was untrained. The maximum likelihood estimation method was used to infer the finger motion. Experimental results have showed that this method could be useful for recognizing finger motions.The average accuracy was as high as 95%.

Decomposition of Surface Electromyographic Signal Using Hidden Markov Model

The detection of physiological signals from the motor system （electromyographic signals） is being utilized in the practice clinic to guide the therapist in a more precise and accurate diagnosis of motor disorders. In this context, the process of decomposition of EMG （electromyographic） signals that includes the identification and classification of MUAP （Motor Unit Action Potential） of a EMG signal, is very important to help the therapist in the evaluation of motor disorders. The EMG decomposition is a complex task due to EMG features depend on the electrode type （needle or surface）, its placement related to the muscle, the contraction level and the health of the Neuromuscular System. To date, the majority of researches on EMG decomposition utilize EMG signals acquired by needle electrodes, due to their advantages in processing this type of signal. However, relatively few researches have been conducted using surface EMG signals. Thus, this article aims to contribute to the clinical practice by presenting a technique that permit the decomposition of surface EMG signal via the use of Hidden Markov Models. This process is supported by the use of differential evolution and spectral clustering techniques. The developed system presented coherent results in： （1） identification of the number of Motor Units actives in the EMG signal; （2） presentation of the morphological patterns of MUAPs in the EMG signal; （3） identification of the firing sequence of the Motor Units. The model proposed in this work is an advance in the research area of decomposition of surface EMG signals.

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 foreanns 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的快递职业上装与肌肉疲劳度关系的研究

为了研究快递员职业上装与肌肉疲劳度之间的关系,以顺丰速运有限公司的夏、秋两套职业装为例,招募了7名男性受试者来模拟快递员的行为特征,测量了受试者工作状态下指伸肌、肱二头肌长头、斜方肌3个肌群的表面肌电信号,运用统计学分析方法分析了工作状态中的肌肉疲劳特征。在模拟快递员工作的实验中,结合受试者的主观分析及表面肌电信号数据可知,肱二头肌长头相比其他测试部位肌肉疲劳感最为强烈,穿着样衣2^(#)时的疲劳感比穿着样衣1^(#)时显著增加(p<0.05)。研究结果可为快递员职业上装的版型设计提供参考。

肌电图(EMG)在运动生物力学研究中的应用

主要通过文献研究,从应用的角度出发,对肌电图(EMG)在运动生物力学研究中的相关研究进行综述。包括EMG的测量、结果的处理与分析、应用研究成果、存在问题和应用展望。重点对目前的研究提出问题并进行探讨,为EMG在运动生物力学中的进一步研究与应用提出思考和帮助。

基于生物力学和颈腰部EMG判别驾驶员疲劳状态

本文中通过采用颈腰部生物力学和表面肌电信号相结合的方式,对驾驶员在驾驶过程中的疲劳状态进行了研究。首先,通过生物力学的计算与分析,合理地选择了能有效反映驾驶疲劳状态的生理信号采集位置,即颈6左右两侧上斜方肌和腰4左右两侧竖脊肌。然后,在利用经验模态分解算法对测得的肌电信号进行去噪的基础上,找出能表征驾驶员疲劳状态的颈腰部肌电特性参数,并对提取的特征参数(颈部复杂度、腰部复杂度和腰部近似熵)进行主成分分析,获得了两个主成分,有效保留有用信息,去除冗余信息,实现了特征参数的降维。最后,以此为自变量建立疲劳驾驶评价模型,有效提高了模型的正确率,加快了模型的运算速度。结果表明,该方法在对驾驶员正常与疲劳状态的区分上具有良好的识别效果,正确率可达90%以上。

Detection of an increase in EMG regularity during fatiguing contractions

The changes in the evolvement patterns of surface electromyography（EMG）signals during both static and dynamic fatiguing contractions are studied.The main finding is that the EMG signal tends to be more and more regular as muscle fatigues.An increase in the summation of all the regular evolvement patterns denoted by Dreg reflects such a tendency.Compared with traditional measurements,Dreg shows less variability among subjects when characterizing a fatigue process.In addition,the calculation of Dreg in the time domain is free from the restrictions disturbing those of spectral parameters.The detection of an increase in the EMG regularity not only proposes a new and easy way to inspect changes in EMG during the fatigue process,but also provides strong supports to estimate muscle fatigue by means of nonlinear analysis methods such as entropy and complexity measures.The detection method of signal regularity can also be applied to other physiological signals.

划船运动员静力及动力性肌肉运动疲劳时肌氧含量的变化特征及对EMG参数的影响

研究目的:1)研究静力和动力性肌肉运动疲劳时肌肉氧含量的变化特点及其规律;2)了解静力性、动力性递增强度运动时EMG参数变化;3)探讨肌氧含量与EMG参数变化之间的关系,为肌肉疲劳时影响肌电-肌氧机制提供可能的理论基础。研究方法:1)肌氧含量的测试:探头纵向旋转让光源和检测器的轴线平行于股外侧肌外侧头大腿测定运动时肌氧含量的变化;2)肌电的测试:采用表面肌电图的测量,得出表面电图各指标参数;3)静力负荷等长收缩:通过力量传感器测出其最大肌肉收缩所对应的MVC;4)动力性负荷运动:采取功率自行车逐级递增负荷的测试方法作为肌肉的动力性运动,同步记录EMG参数,并在每一级负荷末30s采血测定血乳酸浓度。结论:静力性运动时E/T值大幅度增大的时间大多出现在肌氧停止下降之后,肌氧的降低程度与肌肉疲劳程度有关。动力性运动时血乳酸值随负荷而增加,IEMG的变化趋势与血乳酸相似,IEMG、Oxy-Hb、BL值三者呈非常显著性相关,表明肌肉疲劳时肌肉氧供和内环境的改变也是影响EMG信号的重要因素。