The real physical image of the affected limb, which is difficult to move in the traditional mirror training, can be realized easily by the rehabilitation robots. During this training, the affected limb is often in a passive state. However, with the gradual recovery of the movement ability, active mirror training becomes a better choice. Consequently, this paper took the self-developed shoulder joint rehabilitation robot with an adjustable structure as an experimental platform, and proposed a mirror training system completed by next four parts. First, the motion trajectory of the healthy limb was obtained by the Inertial Measurement Units (IMU). Then the variable universe fuzzy adaptive proportion differentiation (PD) control was adopted for inner loop, meanwhile, the muscle strength of the affected limb was estimated by the surface electromyography (sEMG). The compensation force for an assisted limb of outer loop was calculated. According to the experimental results, the control system can provide real-time assistance compensation according to the recovery of the affected limb, fully exert the training initiative of the affected limb, and make the affected limb achieve better rehabilitation training effect.
Wearing transfemoral prosthesis is the only way to complete daily physical activity for amputees. Motion pattern recognition is important for the control of prosthesis, especially in the recognizing swing phase and stance phase. In this paper, it is reported that surface electromyography (sEMG) signal is used in swing and stance phase recognition. sEMG signal of related muscles was sampled by Infiniti of a Canadian company. The sEMG signal was then filtered by weighted filtering window and analyzed by height permitted window. The starting time of stance phase and swing phase is determined through analyzing special muscles. The sEMG signal of rectus femoris was used in stance phase recognition and sEMG signal of tibialis anterior is used in swing phase recognition. In a certain tolerating range, the double windows theory, including weighted filtering window and height permitted window, can reach a high accuracy rate. Through experiments, the real walking consciousness of the people was reflected by sEMG signal of related muscles. Using related muscles to recognize swing and stance phase is reachable. The theory used in this paper is useful for analyzing sEMG signal and actual prosthesis control.
In this paper, a new surface electromyography (sEMG) signal decomposition method based on spatial location is proposed for the high-density sEMG signals in dynamic muscle contraction. Firstly, according to the waveform correlation of each muscle motor units (MU) in each channel, the firing times are extracted, and then the firing times are classified by the spatial location of MU. The MU firing trains are finally obtained. The simulation results show that the accuracy rate of a single MU firing train after classification is more than 91.67%. For real sEMG signals, the accuracy rate to find a same MU by the “two source” method is over (88.3 ± 2.1)%. This paper provides a new idea for dynamic sEMG signal decomposition.
Surface electromyography (sEMG) is a weak signal which is non-stationary and non-periodic. The sEMG classification methods based on time domain and frequency domain features have low recognition rate and poor stability. Based on the modeling and analysis of sEMG energy kernel, this paper proposes a new method to recognize human gestures utilizing convolutional neural network (CNN) and phase portrait of sEMG energy kernel. Firstly, the matrix counting method is used to process the sEMG energy kernel phase portrait into a grayscale image. Secondly, the grayscale image is preprocessed by moving average method. Finally, CNN is used to recognize sEMG of gestures. Experiments on gesture sEMG signal data set show that the effectiveness of the recognition framework and the recognition method of CNN combined with the energy kernel phase portrait have obvious advantages in recognition accuracy and computational efficiency over the area extraction methods. The algorithm in this paper provides a new feasible method for sEMG signal modeling analysis and real-time identification.
The functional coupling between motor cortex and effector muscles during autonomic movement can be quantified by calculating the coupling between electroencephalogram (EEG) signal and surface electromyography (sEMG) signal. The maximal information coefficient (MIC) algorithm has been proved to be effective in quantifying the coupling relationship between neural signals, but it also has the problem of time-consuming calculations in actual use. To solve this problem, an improved MIC algorithm was proposed based on the efficient clustering characteristics of K-means ++ algorithm to accurately detect the coupling strength between nonlinear time series. Simulation results showed that the improved MIC algorithm proposed in this paper can capture the coupling relationship between nonlinear time series quickly and accurately under different noise levels. The results of right dorsiflexion experiments in stroke patients showed that the improved method could accurately capture the coupling strength of EEG signal and sEMG signal in the specific frequency band. Compared with the healthy controls, the functional corticomuscular coupling (FCMC) in beta (14~30 Hz) and gamma band (31~45 Hz) were significantly weaker in stroke patients, and the beta-band MIC values were positively correlated with the Fugl-Meyers assessment (FMA) scale scores. The method proposed in this study is hopeful to be a new method for quantitative assessment of motor function for stroke patients.
To quantitatively evaluate the upper-limb spasticity of stroke patients in recovery stage, the relationship between surface electromyography (sEMG) characteristic indexes from biceps brachii and triceps brachii and the spasticity were explored, which provides the electrophysiological basis for clinical rehabilitation. Ten patients with spasticity after stroke were selected to be estimated by modified Ashworth (MAS) assessment and a passive elbow sinusoidal motion experiment was carried out. At the same time, the sEMG of biceps and triceps were recorded. The results shows that the reflex electromyographic threshold could reflect the physiological mechanism of spasticity and had significant correlation with MAS scale which showed that sEMG could be prosperous for the clinical quantitative evaluation of spasticity of stroke patients.
The present study was carried out with the surface electromyography signal of subjects during the time when subjects did the exercises of the 6 core stability trainings. We analyzed the different activity level of surface electromyography signal, and finally got various fatigue states of muscles in different exercises. Thirty subjects completed exercises of 6 core stability trainings, which were prone bridge, supine bridge, unilateral bridge (divided into two trainings,i.e. the left and right sides alternatively) and bird-dog (divided into two trainings,i.e. the left and right sides alternatively), respectively. Each exercise was held on for 1 minute and 2 minutes were given to relax between two exercises in this test. We measured both left and right sides of the body’s muscles, which included erector spina, external oblique, rectus abdominis, rectus femoris, biceps femoris, anterior tibial and gastrocnemius muscles. We adopted the frequency domain characteristic value of the surface electromyography signal,i.e. median frequency slope to analyze the muscle fatigue in this study. In the present paper, the results exhibit different fatigue degrees of the above muscles during the time when they did the core stability rehabilitation exercises. It could be concluded that supine bridge and unilateral bridge can cause more fatigue on erector spina muscle, prone bridge caused Gastrocnemius muscle much fatigue and there were statistical significant differences (P<0.05) between prone bridge and other five rehabilitation exercises in the degree of rectus abdominis muscle fatigue. There were no statistical significant differences (P>0.05) between all the left and right sides of the same-named muscles in the median frequency slope during all the exercises of the six core stability trainings,i.e. the degree which the various kinds of rehabilitation exercises effected the left and right side of the same-named muscle had no statistical significant difference (P>0.05). In this research, the conclusion presents quantized guidelines on the effects of core stability trainings on different muscles.
Exoskeleton nursing robot is a typical human-machine co-drive system. To full play the subjective control and action orientation of human, it is necessary to comprehensively analyze exoskeleton wearer’s surface electromyography (EMG) in the process of moving patients, especially identifying the spatial distribution and internal relationship of the EMG information. Aiming at the location of electrodes and internal relation between EMG channels, the complex muscle system at the upper limb was abstracted as a muscle functional network. Firstly, the correlation characteristics were analyzed among EMG channels of the upper limb using the mutual information method, so that the muscle function network was established. Secondly, by calculating the characteristic index of network node, the features of muscle function network were analyzed for different movements. Finally, the node contraction method was applied to determine the key muscle group that reflected the intention of wearer’s movement, and the characteristics of muscle function network were analyzed in each stage of moving patients. Experimental results showed that the location of the myoelectric collection could be determined quickly and efficiently, and also various stages of the moving process could effectively be distinguished using the muscle functional network with the key muscle groups. This study provides new ideas and methods to decode the relationship between neural controls of upper limb and physical motion.
Surface electromyogram (sEMG) may have low signal to noise ratios. An adaptive wavelet thresholding technique was developed in this study to remove noise contamination from sEMG signals. Compared with conventional wavelet thresholding methods, the adaptive approach can adjust thresholds based on different signal to noise ratios of the processed signal, thus effectively removing noise contamination and reducing distortion of the EMG signal. The advantage of the developed adaptive thresholding method was demonstrated using simulated and experimental sEMG recordings.
In order to solve the problems of insufficient stimulation channels and lack of stimulation effect feedback in the current electrical stimulation system, a functional array electrode electrical stimulation system with surface electromyography (sEMG) feedback was designed in this paper. Firstly, the effectiveness of the system was verified through in vitro and human experiments. Then it was confirmed that there were differences in the number of amperage needed to achieve the same stimulation stage among individuals, and the number of amperage required by men was generally less than that of women. Finally, it was verified that the current required for square wave stimulation was smaller than that for differential wave stimulation if the same stimulation stage was reached. This system combined the array electrode and sEMG feedback to improve the accuracy of electrical stimulation and performed the whole process recording of feedback sEMG signal in the process of electrical stimulation, and the electrical stimulation parameters could change with the change of the sEMG signal. The electrical stimulation system and sEMG feedback worked together to form a closed-loop electrical stimulation working system, so as to improve the efficiency of electrical stimulation rehabilitation treatment. In conclusion, the functional array electrode electrical stimulation system with sEMG feedback developed in this paper has the advantages of simple operation, small size and low power consumption, which lays a foundation for the introduction of electrical stimulation rehabilitation treatment equipment into the family, and also provides certain reference for the development of similar products in the future.