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.
It is the functional connectivity between motor cortex and muscle that directly relates to the rehabilitation of the dysfunction in upper limbs and neuromuscular activity status, which can be detected by electroencephalogram-electromyography (EEG-EMG) coherence analysis. In this study, based on coherence analysis method, we process the acquisition signals which consist of 9 channel EEG signal from motor cortex and 4 channel EMG signal from forearm, by using 4 groups of hand motions in the healthy subjects, including flexor digitorum, extensor digitorum, wrist flexion, and wrist extension. The results showed that in the β-band, the coherence coefficients between C3 and flexor digitorum (FD) was greater than extensor digitorum (ED) in the right hand flexor digitorum movement; the coherence coefficients between C3 and ED was greater than FD in the right hand extensor digitorum movement; the coherence coefficients between C3 and flexor carpi ulnaris (FCU) was greater than extensor carpi radialis (ECR) in the right hand wrist flexion movement; the coherence coefficients between C3 and ECR was greater than FCU in the right hand wrist extension movement. This analysis provides experimental basis to explore the information decoding of hand motion based on corticomuscular coherence (CMC).
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.
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.
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.
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.
Based on the structure and motion bionic principle of the normal adult fingers, biological characteristics of human hands were analyzed, and a wearable exoskeleton hand function training device for the rehabilitation of stroke patients or patients with hand trauma was designed. This device includes the exoskeleton mechanical structure and the electromyography (EMG) control system. With adjustable mechanism, the device was capable to fit different finger lengths, and by capturing the EMG of the users’ contralateral limb, the motion state of the exoskeleton hand was controlled. Then driven by the device， the user’s fingers conducting adduction/abduction rehabilitation training was carried out. Finally, the mechanical properties and training effect of the exoskeleton hand were verified through mechanism simulation and the experiments on the experimental prototype of the wearable exoskeleton hand function training device.
To better evaluate neuromuscular function of patients with stroke related motor dysfunction, we proposed an effective corticomuscular coherence analysis and coherent significant judgment method. Firstly, the related functional frequency bands in the electroencephalogram (EEG) were extracted via wavelet decomposition. Secondly, coherence were analysed between surface electromyography (sEMG) and sub-bands extracted from EEG. Further more, a coherent significant indicator was defined to quantitatively describe the similarity in certain frequency domain and phase lock activity between EEG and sEMG. Through the analysis of corticomuscular coherence during knee flexion-extension of stroke patients and healthy controls, we found that the stroke patients exhibited significantly lower gamma-band corticomuscular coherence in performing the task with their affected leg, and there was no statistically significant difference between their unaffected lag and the healthy controls, but with the rehabilitation training, the bilateral difference of corticomuscular coherence in patients decreased gradually.