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 contr...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.展开更多
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...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.展开更多
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 pa...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.展开更多
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 c...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.展开更多
基金Project (No. 2005CB724303) supported by the National Basic Re-search Program (973) of China
文摘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.
基金Project supported by the National Natural Science Foundation of China (No. 60171006) and the National Basic Research Program (973) of China (No. 2005CB724303)
文摘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.
基金Supported by the National Basic Research Program("973"Program, No2005CB724303 )
文摘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.
基金supported by the Development of Sleep Disordered Breathing Detection and Auxiliary Regulation System Project(No.2019I1009)。
文摘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.
文摘目的 观察揿针联合通窍利咽方治疗卒中后吞咽障碍的临床疗效及其对患者吞咽肌群表面肌电信号的影响。方法 将90例卒中后吞咽障碍的患者用随机数字表法平均分为常规组与观察组,每组45例。常规组予常规康复训练及饮食护理,观察组在常规组治疗基础上予揿针联合口服通窍利咽方治疗。比较两组临床疗效,观察两组治疗前后美国国立卫生研究院卒中量表(National Institutes of Health stroke scale,NIHSS)、标准吞咽功能评价量表(standardized swallowing assessment,SSA)、改良曼恩吞咽能力评估量表(Mann assessment of swallowing ability,MASA)和吞咽生活质量量表(swallowing-quality of life,SWAL-QOL)的评分变化,观察两组治疗前后舌骨喉活动度检测指标和吞咽肌群表面肌电信号指标的变化,观察两组治疗前后脑血流动力学指标的变化。比较两组不良反应发生情况。结果 观察组总有效率优于常规组(P<0.05)。治疗后,两组NIHSS和SSA评分均较治疗前下降(P<0.05),MASA和SWAL-QOL评分均较治疗前升高(P<0.05);观察组NIHSS、SSA、MASA和SWAL-QOL评分均优于常规组(P<0.05)。治疗后,观察组舌骨上移、舌骨前移、甲状软骨上移和甲状软骨前移距离均大于治疗前(P<0.05),且舌骨上移和舌骨前移距离均大于常规组(P<0.05)。治疗后,两组空吞与吞咽5 mL温水时吞咽肌群表面肌电信号最大振幅、平均振幅和吞咽时间均较治疗前降低(P<0.05),且观察组上述吞咽肌群表面肌电信号指标均低于常规组(P<0.05)。治疗后,两组患者大脑血管平均血流量、平均血流速度、脉搏波速度、血管特性阻抗和外周阻力均较治疗前改善(P<0.05),且观察组上述脑血流动力学指标均优于常规组(P<0.05)。观察组不良反应发生率低于常规组(P<0.05)。结论 在常规康复训练基础上,揿针联合通窍利咽方治疗卒中后吞咽障碍的临床疗效优于单一中药治疗,可促进神经功能恢复,改善患者吞咽功能和吞咽肌群表面肌电信号,提高脑血流动力学指标,且不良反应发生风险较低。
