In this study, surface electromyography (sEMG) of the lower limbs of cerebral-palsy (CP) subjects in gait cycle was recorded and its parameters of gait cycle characters were analyzed to assess their clinical severity. Three algorithms, including integrated profile (IP), sample-entropy (SampEN) and smooth nonlinear energy operator (SNEO) algorithm, were applied to calculate the duration of walking sEMG segments in simulated SEMG signals. After that, the efficiency and accuracy were compared among these three algorithms. SNEO was then selected as the optimal algorithm among the three algorithms and employed for real sEMG signal processing of CP subjects. The results indicated that there was no significant difference in the accuracy of sEMG segement detection for the three algorithms. However, the computation speed of SNEO algorithm was much faster than those of the others and thus it was a suitable algorithm for detecting walking sEMG segments of CP subjects. In addition, the positive correlation was found between the clinical severity and the mean duration of walking sEMG segments in CP subjects. The results indicated that there was a significant difference in the three groups of CP subjects with different levels of severity. Our findings showed that the mean duration of walking sEMG segments could be considered as an assistant index to evaluate the clinical severity of CP subjects.
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.
This study investigated the effect of prolonged walking with load carriage on body posture, muscle fatigue, heart rate and blood pressure of the tested subjects. Ten healthy volunteers performed 30 min walking trials on treadmill (speed=1.1 m/s) with different backpack loads [0% body weight (BW), 10%BW, 15%BW and 20%BW]. The change of body posture, muscle fatigue, heart rate and blood pressure before and after walking and the recovery of muscle fatigue during the rest time (0, 5, 10 and 15 min) were collected using the Bortec AMT-8 and the NDI Optotrak Certus. Results showed that the forward trunk and head angle, muscle fatigue, heart rate and blood pressure increased with the increasing backpack loads and bearing time. With the 20%BW load, the forward angle, muscle fatigue and systolic pressure were significantly higher than with lighter weights. No significantly increased heart rate and diastolic pressure were found. Decreased muscle fatigue was found after removing the backpack in each load trial. But the recovery of the person with 20%BW load was slower than that of 0%BW,10%BW and 15%BW. These findings indicated that the upper limit of backpack loads for college-aged students should be between 15% BW and 20%BW according to muscle fatigue and forward angle. It is suggested that backpack loads should be restricted to no more than 15%BW for walks of up to 30 min duration to avoid irreversible muscle fatigue.
The automatic recognition technology of muscle fatigue has widespread application in the field of kinesiology and rehabilitation medicine. In this paper, we used surface electromyography (sEMG) to study the recognition of leg muscle fatigue during circuit resistance training. The purpose of this study was to solve the problem that the sEMG signals have a lot of noise interference and the recognition accuracy of the existing muscle fatigue recognition model is not high enough. First, we proposed an improved wavelet threshold function denoising algorithm to denoise the sEMG signal. Then, we build a muscle fatigue state recognition model based on long short-term memory (LSTM), and used the Holdout method to evaluate the performance of the model. Finally, the denoising effect of the improved wavelet threshold function denoising method proposed in this paper was compared with the denoising effect of the traditional wavelet threshold denoising method. We compared the performance of the proposed muscle fatigue recognition model with that of particle swarm optimization support vector machine (PSO-SVM) and convolutional neural network (CNN). The results showed that the new wavelet threshold function had better denoising performance than hard and soft threshold functions. The accuracy of LSTM network model in identifying muscle fatigue was 4.89% and 2.47% higher than that of PSO-SVM and CNN, respectively. The sEMG signal denoising method and muscle fatigue recognition model proposed in this paper have important implications for monitoring muscle fatigue during rehabilitation training and exercise.
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.
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.
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.
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%.
To better analyze the problem of abnormal neuromuscular coupling related to motor dysfunction for stroke patients, the functional coupling of the multichannel electromyography (EMG) were studied and the difference between stroke patients and healthy subjects were further analyzed to explore the pathological mechanism of motor dysfunction after stroke. Firstly, the cross-frequency coherence (CFC) analysis and non-negative matrix factorization (NMF) were combined to construct a CFC-NMF model to study the linear coupling relationship in bands and the nonlinear coupling characteristics in different frequency ratios during elbow flexion and extension movement. Furthermore, the significant coherent area and sum of cross-frequency coherence were respectively calculated to quantitatively describe the intermuscular linear and nonlinear coupling characteristics. The results showed that the linear coupling relationship between multichannel muscles was different in frequency bands and the overall coupling was stronger in low frequency band. The linear coupling strength of the stroke patients was lower than that of the healthy subjects in different frequency bands especially in beta and gamma bands. For the nonlinear coupling, the intermuscular coupling strength of stroke patients in different frequency ratios was significantly lower than that of the healthy subjects, and the coupling strength in the frequency ratio 1∶2 was higher than that in the frequency ratio 1∶3. This method can provide a theoretical basis for exploring the intermuscular coupling mechanism of patients with motor dysfunction.
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.