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.
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.
Exercise-induced muscle fatigue is a phenomenon that the maximum voluntary contraction force or power output of muscle is temporarily reduced due to muscular movement. If the fatigue is not treated properly, it will bring about a severe injury to the human body. With multi-channel collection of lower limb surface electromyography signals, this article analyzes the muscle fatigue by adoption of band spectrum entropy method which combined electromyographic signal spectral analysis and nonlinear dynamics. The experimental result indicated that with the increase of muscle fatigue, muscle signal spectrum began to move to low frequency, the energy concentrated, the system complexity came down, and the band spectrum entropy which reflected the complexity was also reduced. By monitoring the entropy, we can measure the degree of muscle fatigue, and provide an indicator to judge fatigue degree for the sports training and clinical rehabilitation training.
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.
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 electromyography (sEMG) has been widely used in the study of clinical medicine, rehabilitation medicine, sports, etc., and its endpoints should be detected accurately before analyzing. However, endpoint detection is vulnerable to electrocardiogram (ECG) interference when the sEMG recorders are placed near the heart. In this paper, an endpoint-detection algorithm which is insensitive to ECG interference is proposed. In the algorithm, endpoints of sEMG are detected based on the short-time energy and short-time zero-crossing rates of sEMG. The thresholds of short-time energy and short-time zero-crossing rate are set according to the statistical difference of short-time zero-crossing rate between sEMG and ECG, and the statistical difference of short-time energy between sEMG and the background noise. Experiment results on the sEMG of rectus abdominis muscle demonstrate that the algorithm detects the endpoints of the sEMG with a high accuracy rate of 95.6%.
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.
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.
Objective To review targeted muscle reinnervation (TMR) surgery for the construction of intelligent prosthetic human-machine interface, thus providing a new clinical intervention paradigm for the functional reconstruction of residual limbs in amputees. MethodsExtensively consulted relevant literature domestically and abroad and systematically expounded the surgical requirements of intelligent prosthetics, TMR operation plan, target population, prognosis, as well as the development and future of TMR. Results TMR facilitates intuitive control of intelligent prostheses in amputees by reconstructing the “brain-spinal cord-peripheral nerve-skeletal muscle” neurotransmission pathway and increasing the surface electromyographic signals required for pattern recognition. TMR surgery for different purposes is suitable for different target populations. Conclusion TMR surgery has been certified abroad as a transformative technology for improving prosthetic manipulation, and is expected to become a new clinical paradigm for 2 million amputees in China.
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.