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.
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.
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.
Lower limb ankle exoskeletons have been used to improve walking efficiency and assist the elderly and patients with motor dysfunction in daily activities or rehabilitation training, while the assistance patterns may influence the wearer’s lower limb muscle activities and coordination patterns. In this paper, we aim to evaluate the effects of different ankle exoskeleton assistance patterns on wearer’s lower limb muscle activities and coordination patterns. A tethered ankle exoskeleton with nine assistance patterns that combined with differenet actuation timing values and torque magnitude levels was used to assist human walking. Lower limb muscle surface electromyography signals were collected from 7 participants walking on a treadmill at a speed of 1.25 m/s. Results showed that the soleus muscle activities were significantly reduced during assisted walking. In one assistance pattern with peak time in 49% of stride and peak torque at 0.7 N·m/kg, the soleus muscle activity was decreased by (38.5 ± 10.8)%. Compared with actuation timing, the assistance torque magnitude had a more significant influence on soleus muscle activity. In all assistance patterns, the eight lower limb muscle activities could be decomposed to five basic muscle synergies. The muscle synergies changed little under assistance with appropriate actuation timing and torque magnitude. Besides, co-contraction indexs of soleus and tibialis anterior, rectus femoris and semitendinosus under exoskeleton assistance were higher than normal walking. Our results are expected to help to understand how healthy wearers adjust their neuromuscular control mechanisms to adapt to different exoskeleton assistance patterns, and provide reference to select appropriate assistance to improve walking efficiency.
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.
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).
In order to help the patients with upper-limb disfunction go on rehabilitation training, this paper proposed an upper-limb exoskeleton rehabilitation robot with four degrees of freedom (DOF), and realized two control schemes, i.e., voice control and electromyography control. The hardware and software design of the voice control system was completed based on RSC-4128 chips, which realized the speech recognition technology of a specific person. Besides, this study adapted self-made surface eletromyogram (sEMG) signal extraction electrodes to collect sEMG signals and realized pattern recognition by conducting sEMG signals processing, extracting time domain features and fixed threshold algorithm. In addition, the pulse-width modulation(PWM)algorithm was used to realize the speed adjustment of the system. Voice control and electromyography control experiments were then carried out, and the results showed that the mean recognition rate of the voice control and electromyography control reached 93.1% and 90.9%, respectively. The results proved the feasibility of the control system. This study is expected to lay a theoretical foundation for the further improvement of the control system of the upper-limb rehabilitation robot.
An in-depth understanding of the mechanism of lower extremity muscle coordination during walking is the key to improving the efficacy of gait rehabilitation in patients with neuromuscular dysfunction. This paper investigates the effect of changes in walking speed on lower extremity muscle synergy patterns and muscle functional networks. Eight healthy subjects were recruited to perform walking tasks on a treadmill at three different speeds, and the surface electromyographic signals (sEMG) of eight muscles of the right lower limb were collected synchronously. The non-negative matrix factorization (NNMF) method was used to extract muscle synergy patterns, the mutual information (MI) method was used to construct the alpha frequency band (8–13 Hz), beta frequency band (14–30 Hz) and gamma frequency band (31–60 Hz) muscle functional network, and complex network analysis methods were introduced to quantify the differences between different networks. Muscle synergy analysis extracted 5 muscle synergy patterns, and changes in walking speed did not change the number of muscle synergy, but resulted in changes in muscle weights. Muscle network analysis found that at the same speed, high-frequency bands have lower global efficiency and clustering coefficients. As walking speed increased, the strength of connections between local muscles also increased. The results show that there are different muscle synergy patterns and muscle function networks in different walking speeds. This study provides a new perspective for exploring the mechanism of muscle coordination at different walking speeds, and is expected to provide theoretical support for the evaluation of gait function in patients with neuromuscular dysfunction.
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.
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.