一种基于肌动信号的股四头肌收缩力量估计方法研究

针对可穿戴设备及共融机器人中的力/力矩测量需求,提出了一种基于相关向量机的人体股四头肌收缩力量估计方法,该方法具备采集设备安装方便、鲁棒性强且宜人性好等优点。通过采集人体股四头肌主要肌肉的MMG信号,提取平均绝对值MAV、平均功率频率MPF、样本熵Samp En及2个不同通道MMG信号之间的相关系数CC2Cs 4个特征,利用基于稀疏贝叶斯理论的相关向量机算法RVM构建了MMG-肌肉收缩力量模型,并验证了所提方法的有效性和准确度。结果表明,同一参与者的模型估计结果的均方根误差RMSE为8.7%MVC(最大肌肉随意收缩力),决定系数R^2为0.817,该方法是一种有效、适宜应用在可穿戴设备的人体股四头肌收缩力量估计方法。

基于肌动图与肌电图信号的假肢控制系统的研究

目的验证使用肌动图(mechanomyography,MMG)和肌电图(electromyography,EMG)两种信号共同作为假肢控制信号时,是否能提高假肢控制系统分类的准确度。方法本文采用信号融合方法,通过融合6通道的MMG信号与2通道的EMG信号,以及基于模式识别的线性判别分析(lineardiscriminant analysis,LDA)算法,研制了基于MMG和EMG信号的假肢控制系统。结果该系统能对采集到的信号进行处理并得出动作分类结果,然后控制假肢完成相应动作。对6位测试者的腕屈、腕伸、张开、握拳4类动作以及静止状态进行假肢控制的动作分类准确度实验,准确度达94.6%,比单独用MMG信号的精度88.5%或EMG信号精度90.4%效果更好。结论基于MMG与EMG信号的假肢控制系统可以更好地实现假肢控制动作的有效分类,未来可应用于上臂截肢的残疾人。

脑卒中早期上肢肌动图信号与上肢上臂肌痉挛的关系及风险预测模型构建

目的探讨脑卒中早期上肢肌动图信号与上肢上臂肌痉挛发生的关系及其风险预测模型。方法选取2019-06—2021-06北京康复医院收治脑卒中患者196例,根据患者是否发生上肢屈肌痉挛分为痉挛组和非痉挛组,比较2组患者早期上肢肌动图信号变化和临床资料变化,多因素Logistic回归分析脑卒中合并上肢上臂肌痉挛发生的危险因素并建立风险预测模型,该模型的区分度用受试者工作特征曲线(ROC)评估,拟合度采用Hosmer-Lemeshow test。结果痉挛组上肢肱二头肌的伸展iEMG、协同收缩率和肱三头肌协同收缩率均高于非痉挛组(P<0.001),肱二头肌屈曲iEMG和肱三头肌的屈曲iEMG、伸展i EMG均低于非痉挛组(P<0.001),男性、年龄<60岁、卒中病程≥3个月、颅内手术史、疼痛、大面积病变和美国国立卫生院卒中量表(NIHSS)评分≥13分患者占比均高于非痉挛组(P<0.05)。男性、年龄<60岁、卒中病程≥3个月、疼痛和NIHSS评分≥13分是脑卒中患者上肢上臂肌痉挛的独立危险因素,低、中、高风险脑卒中患者上肢上臂肌痉挛发生率分别为5.38%、38.24%、85.71%,差异有统计意义(χ^(2)=79.023,P<0.001),建立的脑卒中患者上肢上臂肌痉挛风险预测ROC曲线下面积(AUC)为0.901(95%CI:0.835~0.967,P<0.001),Hosmer-Lemeshow test P=0.168。结论脑卒中合并上肢屈肌痉挛患者早期上臂功能下降,性别、年龄、卒中病程、疼痛以及NIHSS评分是其独立危险因素,建立的风险预测模型的预测效能高。

等张收缩肱二头肌特性的多信号结合研究

表面肌电图(Surface Electromyography,SEMG)表征了骨骼肌的电生理特性;而肌动图(Mechanomyography,MMG)则是反映骨骼肌收缩时的肌纤维振动的力学特性。超声可以清晰的观测骨骼肌空间形态变化,我们把超声扫描骨骼肌得到的有关骨骼肌结构形态变化的信息定义为"声肌图(Sonomyography,SMG)"。本文同步采集了肘关节屈伸引起的肱二头肌等张收缩时的超声图像、肘关节角度、SEMG和MMG信号,初步分析了不同信号之间的关系,多角度结合研究了骨骼肌的特性,表明了多信号结合研究骨骼肌特性是发展的趋势。

Classification of forearm action surface EMG signals based on fractal dimension

Surface electromyogram （EMG） signals were identified by fractal dimension.Two patterns of surface EMG signals were acquired from 30 healthy volunteers＇ right forearm flexor respectively in the process of forearm supination （FS） and forearm pronation （FP）.After the raw action surface EMG （ASEMG） signal was decomposed into several sub-signals with wavelet packet transform （WPT）,five fractal dimensions were respectively calculated from the raw signal and four sub-signals by the method based on fuzzy self-similarity.The results show that calculated from the sub-signal in the band 0 to 125 Hz,the fractal dimensions of FS ASEMG signals and FP ASEMG signals distributed in two different regions,and its error rate based on Bayes decision was no more than 2.26%.Therefore,the fractal dimension is an appropriate feature by which an FS ASEMG signal is distinguished from an FP ASEMG signal.

Motion Classification of EMG Signals Based on Wavelet Packet Transform and LS-SVMs Ensemble

This paper presents an effective method for motion classification using the surface electromyographic (sEMG) signal collected from the forearm. Given the nonlinear and time-varying nature of EMG signal, the wavelet packet transform (WPT) is introduced to extract time-frequency joint information. Then the multi-class classifier based on the least squares support vector machine (LS-SVM) is constructed and verified in the various motion classification tasks. The results of contrastive experiments show that different motions can be identified with high accuracy by the presented method. Furthermore, compared with other classifiers with different features, the performance indicates the potential of the SVM techniques combined with WPT in motion classification.

Joint application of feature extraction based on EMD-AR strategy and multi-class classifier based on LS-SVM in EMG motion classification

This paper presents an effective and efficient combination of feature extraction and multi-class classifier for motion classification by analyzing the surface electromyografic(sEMG) signals. In contrast to the existing methods,considering the non-stationary and nonlinear characteristics of EMG signals,to get the more separable feature set,we introduce the empirical mode decomposition(EMD) to decompose the original EMG signals into several intrinsic mode functions(IMFs) and then compute the coefficients of autoregressive models of each IMF to form the feature set. Based on the least squares support vector machines(LS-SVMs) ,the multi-class classifier is designed and constructed to classify various motions. The results of contrastive experiments showed that the accuracy of motion recognition is improved with the described classification scheme. Furthermore,compared with other classifiers using different features,the excellent performance indicated the potential of the SVM techniques embedding the EMD-AR kernel in motion classification.

A Kinematic Analysis of Joint Synergy of the Lower Limb in Human Locomotion

This paper deals with the mode analysis of the kinematic structure of human locomotion. The authors investigated synergy mechanism of human locomotion from motion-captured data and EMG signal data. The authors extracted some common basic movements and residual modes, and analyzed the kinematical structures of limit cycle in joint angle space. The authors also implemented the numerical simulation analyses by using the motion captured data and EMG signal data to investigate the mechanical activities of human joints and to extract the mechanical structure of the limit cycle. The results show the joint synergy that is derived by the common basic modes, which expresses an inverted pendulum mode in support phase, and ballistic mode in swing phase with the kick-off motion in the most effective direction. This result can be guessed that the control strategy of human locomotion is simply based on the minimal control principle.

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%.

A Study on sEMG Simulation Modeling and Its Decomposition Methods

In this paper, we establish a surface electromyography(sEMG) signal model and study the signal decomposition method from noisy background. Firstly, single fiber action potential (SFAP), motor unit action potential (MUAP) and motor unit action potential train(MUAPT) are simulated based on the tripolar signal source model, and then the sEMG is obtained; secondly, the simulated sEMG signal is extracted from the mixed signals that consists of white noises, power frequency interference signal and electrocardio signal by independent component analysis (ICA) algorithms; lastly, the spikes corresponding to each motor unit action potential from the simulated sEMG signals were detected by applying the wavelet transform (WT) method. Simulation results showed that sEMG model could describe the physiological process of sEMG, ICA and WT methods could extract the sEMG signal and its features, which will lay a foundation for further classifying the MUAP.

Spectral and mathematical evaluation of electromyography signals for clinical use

The surface electromyography （SEMG） is a complicated biomedical signal, generated during voluntary or involuntary muscle activities and these muscle activities are always controlled by the nervous system. In this paper, the processing and analysis of SEMG signals at multiple muscle points for different operations were carried out. Myoelectric signals were detected using designed acquisition setup which consists of an instrumenta- tion amplifier, filter circuit, an amplifier with gain adjustment. Fhrther, Labview^-based data programming code was used to record SEMG signals for independent activities. The whole system consists of bipolar noninvasive electrodes, signal acquisition protocols and signal conditioning at different levels. This work uses recorded SEMG signals generated by biceps and triceps muscles for four different arm activities. Feature extraction was done on the recorded signal for investigating the voluntary muscular contraction relationship for exercising statistic measured index method to evaluate distance between two independent groups by directly addressing the quality of signal in separability class for different arm movements. Thereafter repeated factorial analysis of variance technique was implemented to evaluate the effectiveness of processed signal. From these results, it demonstrates that the proposed method can be used as SEMG feature evaluation index.